Machine learning algorithms for data analytics
    21Mar, 2024
    Mastering Machine Learning: The Top 10 Algorithms You Need to Know

    In today’s data-driven world, machine learning has become an indispensable tool for extracting valuable insights from vast amounts of data. However, for beginners entering the realm of machine learning, the plethora of algorithms available can seem overwhelming. Understanding the fundamental algorithms is crucial for building a strong foundation in this field.

     

    Machine learning algorithms for data analytics

    Linear Regression

    Linear regression is a statistical method used in machine learning to predict continuous outcomes or values based on input features. Let’s break down how linear regression works in simple steps:

    Understanding the Concept:

    Imagine you have some data where you know the input (like the size of a house) and the output (like the price of the house). Linear regression helps us find the relationship between these inputs and outputs.


    Plotting the Data:

    We start by plotting our data points on a graph, with the input values on the x-axis and the corresponding output values on the y-axis. This gives us a scatterplot of our data points.


    Drawing a Line:

    Linear regression finds the best-fitting line through the data points. This line represents the relationship between the input and output variables. The goal is to find the line that minimizes the difference between the actual data points and the predicted values on the line.


    The Equation of the Line:

    The equation of the line in linear regression is represented as:
    Y = mX + c
    Y represents the predicted output value.
    X represents the input feature.
    m is the slope of the line, which represents how much the output value changes for a one-unit change in the input feature.
    c is the y-intercept, which represents the value of Y when X is 0.


    Finding the Best-Fitting Line:

    Linear regression uses a method called least squares to find the best-fitting line. It calculates the sum of the squares of the vertical distances between the data points and the line. The line that minimizes this sum is considered the best-fitting line.


    Making Predictions:

    Once we have the equation of the best-fitting line, we can use it to make predictions. Given a new input value, we can plug it into the equation to predict the corresponding output value.


    Evaluating the Model:

    Finally, we evaluate the performance of our linear regression model by comparing the predicted values to the actual values in our dataset. Common evaluation metrics include mean squared error (MSE) or R-squared.


    Extensions to Complex Models:

    While linear regression is simple and easy to understand, it forms the basis for more complex regression techniques like polynomial regression, ridge regression, or lasso regression, which can handle more intricate relationships between variables.


    In summary, linear regression is a powerful tool for predicting continuous values based on input features. By finding the best-fitting line through the data points, it allows us to understand and quantify relationships between variables in our dataset.

     

     

    Logistic regression

    Logistic regression is a fundamental algorithm in machine learning, primarily used for binary classification problems. Let’s delve into how logistic regression works step by step, explained in simple terms:

    Binary Classification:

    Binary classification means we have two possible outcomes for our prediction: yes or no, 0 or 1, true or false. For example, predicting whether an email is spam or not spam, whether a transaction is fraudulent or legitimate, etc.


    Understanding Probability:

    Logistic regression predicts the probability of one of the two outcomes occurring. Instead of directly predicting 0 or 1, it predicts a probability value between 0 and 1.


    Sigmoid Function:

    Logistic regression uses a special function called the sigmoid function (also known as the logistic function). This function maps any real-valued number to a value between 0 and 1. The formula for the sigmoid function is:

    Here, “x” represents the input value, and “σ(x)” represents the output, which is the probability of the event occurring.


    Linear Equation:

    Like linear regression, logistic regression also has an equation, but it’s slightly different. Instead of a straight line, logistic regression uses a line that’s curved due to the sigmoid function. The equation looks like this:
    Logistic Regression Equation
    Here, “z” represents the linear combination of input features and their respective coefficients, just like in linear regression.


    Probability Interpretation:

    Once we have the output from the sigmoid function (which is a probability), we can interpret it as follows: if the probability is closer to 0, it means the event is less likely to occur, and if it’s closer to 1, it means the event is more likely to occur.


    Decision Boundary:

    Logistic regression separates the input space into two regions using a decision boundary. If the probability is above a certain threshold (usually 0.5), we predict one class (e.g., 1), and if it’s below the threshold, we predict the other class (e.g., 0).


    Training the Model:

    During training, logistic regression adjusts its coefficients to minimize the difference between the predicted probabilities and the actual outcomes in the training data. It does this using optimization algorithms like gradient descent.


    Evaluation:

    To evaluate the logistic regression model, we use metrics like accuracy, precision, recall, F1 score, etc., depending on the specific problem and requirements.


    In summary, logistic regression is a powerful algorithm for binary classification problems, predicting the probability of an event occurring and making decisions based on that probability.

     

     

    Decision Trees

    Decision trees are versatile and intuitive models used in machine learning for both classification and regression tasks. Let’s explore how decision trees work in a step-by-step manner, emphasizing their simplicity and interpretability:

    Intuitive Representation:

    Decision trees mimic the human decision-making process by breaking down complex decision-making into a series of simple questions. Each node in the tree represents a decision based on a feature, and each branch represents the possible outcomes of that decision.


    Feature Space Partitioning:

    Decision trees partition the feature space into regions by recursively splitting the data based on feature values. At each step, the algorithm selects the feature that best separates the data into distinct classes or groups.


    Splitting Criteria:

    The decision tree algorithm evaluates different splitting criteria to determine the best feature and threshold for splitting the data at each node. Common splitting criteria include Gini impurity for classification tasks and mean squared error for regression tasks.


    Building the Tree:

    The decision tree algorithm continues to split the data recursively until certain stopping criteria are met, such as reaching a maximum depth, minimum number of samples per leaf node, or no further improvement in purity or error reduction.


    Leaf Nodes:

    Once the data is partitioned into homogeneous subsets or reaches the stopping criteria, the algorithm assigns a class label (for classification) or predicts a continuous value (for regression) at the leaf nodes of the tree.


    Interpretability:

    One of the key advantages of decision trees is their interpretability. The decision rules at each node can be easily understood and visualized, making it straightforward to interpret the model’s predictions and understand the factors influencing them.


    Handling Categorical and Numerical Features:

    Decision trees can handle both categorical and numerical features without requiring feature scaling or one-hot encoding. They automatically select the best split points for numerical features and perform multiway splits for categorical features.


    Handling Missing Values:

    Decision trees can handle missing values in the dataset by selecting the best split based on the available data, allowing for robust performance in real-world datasets with incomplete information.


    Ensemble Methods:

    Decision trees can be combined into ensemble methods such as random forests and gradient boosting, further improving predictive performance and generalization while retaining interpretability to some extent.


    In summary, decision trees are powerful and intuitive models that partition the feature space into regions, making them suitable for both classification and regression tasks. Their simplicity, interpretability, and ability to handle a variety of data types make them a popular choice in many machine learning applications.

    Random Forest

    Random Forest is a robust and versatile ensemble learning technique that leverages the power of multiple decision trees to enhance predictive accuracy and mitigate overfitting. Here’s an explanation of how Random Forest works:

    Ensemble Learning:

    Random Forest belongs to the family of ensemble learning algorithms. Ensemble learning combines the predictions of multiple individual models to produce a more accurate and robust final prediction.


    Construction of Decision Trees:

    Random Forest constructs a predefined number of decision trees, typically referred to as “trees” or “estimators”. Each decision tree is built using a random subset of the training data and a random subset of the features.


    Randomness and Diversity:

    The randomness introduced in building individual trees helps to create diversity among them. Each tree may focus on different subsets of features and data instances, capturing different aspects of the underlying patterns in the data.


    Bagging (Bootstrap Aggregating):

    Random Forest employs a technique called bagging, where each decision tree is trained on a bootstrap sample of the training data. Bootstrap sampling involves randomly selecting data points with replacement, allowing some instances to be selected multiple times and others not at all.


    Feature Randomness:

    In addition to using bootstrap samples, Random Forest also introduces randomness in feature selection for each split of the decision tree. Instead of considering all features at each split, a random subset of features is considered, further enhancing diversity among the trees.


    Combining Predictions:

    Once all the decision trees are trained, Random Forest combines their predictions to make the final prediction. For classification tasks, it typically uses a majority voting scheme, where the class predicted by the majority of trees is chosen. For regression tasks, it averages the predictions made by individual trees.


    Advantages of Random Forest:

    Random Forest is highly resistant to overfitting due to the averaging effect of multiple trees.
    It performs well on a wide range of datasets and can handle high-dimensional feature spaces.
    Random Forest provides estimates of feature importance, allowing insights into the relative importance of different features in making predictions.


    Hyperparameters:

    Random Forest has several hyperparameters that can be tuned to optimize performance, such as the number of trees, maximum depth of trees, and the number of features considered at each split.
    In summary, Random Forest is a powerful ensemble learning technique that combines the predictions of multiple decision trees to achieve higher accuracy and reduce the risk of overfitting, making it a popular choice for various machine learning tasks.

     

     

    random forest

    Support Vector Machines (SVM)

    Support Vector Machines (SVM) is a versatile machine learning algorithm used for both classification and regression tasks. Let’s break down how SVM works:

    Classification and Regression:

    SVM can be used for both classification and regression tasks. In classification, SVM aims to classify data points into different categories, while in regression, it predicts continuous outcomes.


    Hyperplane:

    At the core of SVM is the concept of a hyperplane, which is a decision boundary that separates different classes in the feature space. For a binary classification problem, the hyperplane is a line in two dimensions, a plane in three dimensions, and a hyperplane in higher dimensions.


    Maximizing Margin:

    SVM finds the hyperplane that maximizes the margin, which is the distance between the hyperplane and the closest data points (support vectors) from each class. Maximizing the margin helps SVM generalize well to unseen data and improves its performance.


    Support Vectors:

    Support vectors are the data points that lie closest to the hyperplane and influence its position. These points are crucial in defining the decision boundary and are used to maximize the margin.


    Kernel Trick:

    SVM can efficiently handle non-linearly separable data by mapping the input features into a higher-dimensional space using a kernel function. The kernel function computes the dot product between feature vectors in the higher-dimensional space without explicitly transforming them. Common kernel functions include linear, polynomial, and radial basis function (RBF) kernels.


    C Parameter:

    SVM has a regularization parameter, often denoted as C, that controls the trade-off between maximizing the margin and minimizing the classification error. A smaller value of C allows for a wider margin but may lead to misclassification of some data points, while a larger value of C reduces the margin to classify more data points correctly.


    Soft Margin SVM:

    In cases where the data is not linearly separable or contains outliers, SVM uses a soft margin approach. Soft margin SVM allows for some misclassification errors by introducing slack variables, which penalize data points that fall on the wrong side of the margin or hyperplane.


    Regression with SVM:

    In regression tasks, SVM aims to find a hyperplane that best fits the data points while minimizing the error between the predicted and actual values. The epsilon-insensitive loss function is used to define a margin of tolerance around the fitted hyperplane.


    In summary, Support Vector Machines (SVM) is a powerful algorithm for both classification and regression tasks. By finding the hyperplane that best separates different classes in the feature space, SVM achieves effective decision boundaries and generalization performance.

     

     

    K-Nearest Neighbors (KNN)

    K-Nearest Neighbors (KNN) is a straightforward yet powerful algorithm used for classification and regression tasks. Let’s explore how KNN works in simple terms:

    Nearest Neighbor Classification:

    In KNN, the classification of a data point is determined by the majority class among its k nearest neighbors in the feature space.


    Distance Metric:

    KNN calculates the distance between data points using a chosen distance metric, commonly the Euclidean distance. Other distance metrics such as Manhattan distance or cosine similarity can also be used depending on the nature of the data.


    Choosing ‘k’:

    The value of ‘k’ represents the number of nearest neighbors to consider when making a prediction. It is an important hyperparameter in KNN and can significantly impact the model’s performance.


    Classification Decision:

    Once the ‘k’ nearest neighbors are identified, KNN takes a majority vote among them to determine the class of the data point being classified. The class with the highest number of occurrences among the neighbors is assigned to the data point.


    Handling Ties:

    In cases where there is a tie among classes, KNN may employ various tie-breaking strategies such as selecting the class of the nearest neighbor or assigning equal weights to all neighbors.


    Training Phase:

    KNN is a lazy learner, meaning it does not explicitly learn a model during the training phase. Instead, it stores all training data points and their corresponding class labels in memory for later use during classification.


    Testing Phase:

    During the testing phase, KNN calculates the distance between the test data point and all training data points. It then identifies the ‘k’ nearest neighbors and predicts the class based on their majority vote.


    Regression with KNN:

    In addition to classification, KNN can also be used for regression tasks. Instead of taking a majority vote, KNN calculates the average (or weighted average) of the target values of its ‘k’ nearest neighbors to predict the continuous value for the test data point.


    In summary, K-Nearest Neighbors (KNN) is a simple yet effective algorithm for classification and regression tasks. By relying on the majority class of its nearest neighbors, KNN provides an intuitive and interpretable approach to making predictions in the feature space.

    Restaurant Order Datasets and Mastering Data Visualization with Python Charts
    2Dec, 2023
    Merging Restaurant Order Datasets and Mastering Data Visualization with Python Charts

    Dataset Link

    dataset Link

    This dataset have two table 

    menu_items Table:

    • menu_item_id: Unique identifier for each menu item. This is often used as the primary key.
    • item_name: The name of the menu item, providing a human-readable description.
    • category: Indicates the category or type of cuisine to which the menu item belongs.
    • price: The cost of the menu item, usually in US Dollars.

    order_details Table:

    • order_details_id: A unique identifier for each item within an order. This could be the primary key for this table.
    • order_id: Identifies the order to which the item belongs. This likely corresponds to a unique order placed by a customer.
    • order_date: The date when the order was placed, usually in the format MM/DD/YY.
    • order_time: The time when the order was placed, typically in the format HH:MM:SS AM/PM.
    • item_id: This field links to the “menu_item_id” in the “menu_items” table, establishing a relationship between the two tables. It indicates which menu item was included in the order.

    Read CSV files 

    import pandas as pd


order_details = pd.read_csv(r'C:\Users\yogesh\Downloads\Restaurant+Orders+CSV\order_details.csv')

# Read menu details from CSV file
Menu_detail = pd.read_csv(r'C:\Users\yogesh\Downloads\Restaurant+Orders+CSV\menu_items.csv')

# Display order details DataFrame
print("Order Details:")
print(order_details)

# Display menu details DataFrame
print("\nMenu Details:")
print(Menu_detail)

    Merge DataFrames 

    # Merge DataFrames based on 'item_id' and 'menu_item_id'
merged_df = pd.merge(order_details, Menu_detail, left_on='item_id', right_on='menu_item_id', how='inner')

# Display the resulting merged DataFrame
print("Merged DataFrame:")
print(merged_df)

    pd.merge(order_details, Menu_detail, left_on=’item_id’, right_on=’menu_item_id’, how=’inner’):

    • This line uses the pd.merge function from the pandas library to merge two DataFrames (order_details and Menu_detail) based on specific columns.
      order_details is the left DataFrame, and Menu_detail is the right DataFrame.
    • The left_on parameter specifies the column in the left DataFrame to use for the merge operation (‘item_id’ in this case).
    • The right_on parameter specifies the column in the right DataFrame to use for the merge operation (‘menu_item_id’ in this case).
    • The how parameter specifies the type of merge to be performed. In this case, it’s an inner join (how=’inner’), which means only the common rows between the two DataFrames will be included in the result.
      print(“Merged DataFrame:”):
    • This line simply prints a descriptive message indicating that the following output is the merged DataFrame.
      print(merged_df):
    • This line prints the resulting merged DataFrame (merged_df) that was obtained from the pd.merge operation.

    In summary, this code is merging two DataFrames (order_details and Menu_detail) based on the ‘item_id’ and ‘menu_item_id’ columns using an inner join and then printing the resulting merged DataFrame. The inner join ensures that only the rows with matching ‘item_id’ and ‘menu_item_id’ values in both DataFrames are included in the final result.

    merged_df.info()

    merged_df.info()

    It seems like you’ve mentioned merged_df.info() in Python, which is likely a command to display information about a DataFrame using the Pandas library. The info() method provides a concise summary of a DataFrame, including information about the data types, non-null values, and memory usage.

    Here’s a breakdown of what each part of the output typically includes:

    • Index: Information about the index, including its type and the number of non-null values.
    • Columns: A list of all columns in the DataFrame, along with the count of non-null values and the data type of each column.
    • dtypes: Data types of each column.
    • Memory Usage: Total memory usage by the DataFrame.
    • Non-Null Count: Number of non-null (non-missing) values in each column.

    Price for Analysis

    Total_Sales=merged_df['price'].sum()
print(Total_Sales)

    The variable Total_Price will contain the sum of all the values in the ‘price’ column.
    Answer is 159217.89999999997

    Average Price

     

    Average_Price=merged_df['price'].mean()
print(Average_Price)

    13.16176738034223

    total number of Orders

    Total_order=merged_df['order_details_id'].count()
print(Total_order)

    12097

     

    Number of orders by Items

    item_counts=merged_df.groupby('item_name')['order_details_id'].count().reset_index()
print("No of order by Item")
print(item_counts)

    This code appears to be written in Python and is likely part of a data analysis or manipulation script, particularly using the pandas library. Let me break down what this code is doing:

    • merged_df: This is presumably a pandas DataFrame that has been previously defined or loaded. The code is referring to a column named ‘item_name’ and ‘order_details_id’ within this DataFrame.
    • groupby(‘item_name’): This is a pandas operation that groups the DataFrame by unique values in the ‘item_name’ column. Essentially, it’s creating groups where each group contains rows with the same ‘item_name’.
    • [‘order_details_id’].count(): For each group, it’s counting the number of occurrences of ‘order_details_id’. This essentially gives the number of orders for each unique item.
    • .reset_index(): After the grouping and counting, the result is a new DataFrame with ‘item_name’ as an index and the count of orders as a column. The reset_index() method is used to move ‘item_name’ back to a regular column, and a new default integer index is assigned.
    • print(“No of order by Item”): This line is simply printing a header or title for the output to indicate that what follows is the count of orders by item.
    • print(item_counts): Finally, this line prints the DataFrame item_counts, which now contains two columns: ‘item_name’ and the count of orders for each item.

    In summary, the code is grouping the DataFrame by ‘item_name’, counting the number of orders for each item, and then printing the resulting DataFrame that shows the count of orders for each unique item.

    number of order by items

    Top 5 items by orders

    top_5_items =item_counts.sort_values(by='order_details_id',ascending=False).head(5)
print(top_5_items)

    Let me explain each line:

    • sort_values(by=’order_details_id’, ascending=False): This line is sorting the DataFrame item_counts based on the ‘order_details_id’ column in descending order (ascending=False). This means that items with the highest number of orders will appear at the top of the DataFrame.
    • .head(5): After sorting, this line selects the first 5 rows of the DataFrame, effectively giving you the top 5 items with the highest order counts.
    • print(top_5_items): Finally, this line prints the resulting DataFrame top_5_items, which now contains the top 5 items with the highest order counts.

    So, when you run this code after the previous one, it will display the top 5 items based on the number of orders, with the highest count appearing first.

    top 5 items by orders

    BAR CHART FOR TOP 5 ITEMS

    import matplotlib.pyplot as plt
plt.bar(top_5_items.item_name,top_5_items.order_details_id)
plt.xlabel("name of items")
plt.ylabel("Numbers of orders")
plt.title("Top 5 items order ")
plt.show()
    • Importing Matplotlib:import matplotlib.pyplot as pltImports the Matplotlib library and aliases it as plt.

    Creating the Bar Chart:

    • plt.bar(top_5_items.item_name, top_5_items.order_details_id)
      Uses the bar function to create a bar chart.
      Takes ‘item_name’ as x-axis values and ‘order_details_id’ as y-axis values from the top_5_items DataFrame.
      Setting X and Y Labels:
    • plt.xlabel(“Name of Items”)
      Sets the label for the x-axis as “Name of Items”.
      plt.ylabel(“Number of Orders”)
      Sets the label for the y-axis as “Number of Orders”.
      Setting the Title:
    • plt.title(“Top 5 Items Orders”)
      Sets the title of the plot as “Top 5 Items Orders”.
      Displaying the Plot:
    • plt.show()
      Displays the plot.
    top 5 items by orders barchart

    Bottom 5 Items by orders

    print("bottom 5 Items by orders")
bottom_5_items =item_counts.sort_values(by='order_details_id',ascending=True).head(5)
print(bottom_5_items)

plt.bar(bottom_5_items.item_name,bottom_5_items.order_details_id)
plt.xlabel("name of items")
plt.ylabel("Numbers of orders")
plt.title("bottom_5_items ")
plt.show()
    • Print Statement: print(“bottom 5 Items by orders”)Prints a message indicating that the following information is about the bottom 5 items by orders.
    • Sorting and Selecting Bottom 5 Items: bottom_5_items = item_counts.sort_values(by=’order_details_id’, ascending=True).head(5)item_counts is assumed to be a DataFrame with information about items and order details.
    • Sorts the DataFrame based on the ‘order_details_id’ column in ascending order.Selects the first 5 rows (bottom 5 after sorting) using head(5).
      Stores the result in the bottom_5_items variable.
      Printing the Bottom 5 Items:
    • print(bottom_5_items) Prints the DataFrame containing information about the bottom 5 items by orders.
      Creating and Displaying the Bar Chart:
    • plt.bar(bottom_5_items.item_name, bottom_5_items.order_details_id)
      Creates a bar chart using Matplotlib.
    • Uses ‘item_name’ column as x-axis values and ‘order_details_id’ column as y-axis values from the bottom_5_items DataFrame.
      plt.xlabel(“name of items”)
    • Sets the label for the x-axis as “name of items”.
    • plt.ylabel(“Numbers of orders”)
      Sets the label for the y-axis as “Numbers of orders”.
    • plt.title(“bottom_5_items “)
      Sets the title of the plot as “bottom_5_items “.
      plt.show()
      Displays the created bar chart
    bottom 5 items by orders

    Number of Order by Category

    print("Number of Order by Category")
Category_order_count=merged_df.groupby('category')['order_details_id'].count().reset_index()
print(Category_order_count)
plt.barh(Category_order_count.category,Category_order_count.order_details_id)
plt.xlabel("orders")
plt.ylabel("Name of category ")
plt.title("Order numbers ")
plt.show()
    • Data Aggregation: Category_order_count = merged_df.groupby(‘category’)[‘order_details_id’].count().reset_index(): This line groups the merged dataset (merged_df) by the ‘category’ column and counts the number of occurrences of ‘order_details_id’ in each category. The result is stored in a new DataFrame called Category_order_count.
    • Printing the DataFrame: print(Category_order_count): This line prints the Category_order_count DataFrame, displaying the number of orders for each category.
    • Plotting a Horizontal Bar Chart: plt.barh(Category_order_count.category, Category_order_count.order_details_id): This line creates a horizontal bar chart using Matplotlib (plt). It takes the category names on the y-axis (Category_order_count.category) and the corresponding order counts on the x-axis (Category_order_count.order_details_id).
    • Customizing the Chart: plt.xlabel(“orders”): Sets the label for the x-axis as “orders.”
    • plt.ylabel(“Name of category”): Sets the label for the y-axis as “Name of category.” plt.title(“Order numbers”): Sets the title of the chart as “Order numbers.”
    • Displaying the Chart: plt.show(): Finally, this line displays the generated horizontal bar chart with the specified labels and title.

    In summary, the code calculates and visualizes the number of orders for each category in a horizontal bar chart, providing a quick and visually accessible way to understand the distribution of orders across different categories.

    Remember to ensure that you have the necessary libraries imported at the beginning of your script, such as:

    no of order by category in restaurant data

    Category wise chart on matplotlib

    plt.barh(Category_order_count.category, Category_order_count.order_details_id):

    This line creates a horizontal bar chart using the barh function from Matplotlib.
    The first argument, Category_order_count.category, represents the categories and is used for the y-axis.
    The second argument, Category_order_count.order_details_id, represents the number of orders and is used for the x-axis.
    plt.xlabel(“orders”):

    This line sets the label for the x-axis to “orders.”
    plt.ylabel(“Name of category “):

    This line sets the label for the y-axis to “Name of category.”
    plt.title(“Order numbers “):

    This line sets the title of the chart to “Order numbers.”
    plt.show():

    Finally, this line displays the generated horizontal bar chart with the specified labels and title.
    In summary, this code segment is a concise way to visualize the number of orders for each category in a horizontal bar chart, with clear labels and a title for better interpretation.

    Convert hours group from hours minutes seconds

    merged_df['order_time']=pd.to_datetime(merged_df['order_time'])
merged_df['Hours']=merged_df['order_time'].dt.hour
print(merged_df['Hours'])

    Convert ‘order_time’ to Datetime Format:

    • merged_df[‘order_time’] = pd.to_datetime(merged_df[‘order_time’])
      Converts the ‘order_time’ column in the DataFrame merged_df to a datetime format using the pd.to_datetime() function.
      Extract Hour Component:
    • merged_df[‘Hours’] = merged_df[‘order_time’].dt.hour
      Creates a new column ‘Hours’ in the DataFrame merged_df.
      Utilizes the dt.hour accessor to extract the hour component from the ‘order_time’ column.
      Print the Extracted Hour Values:
    • print(merged_df[‘Hours’])
      Prints the values in the newly created ‘Hours’ column, displaying the hour component of each order’s timestamp.
      In essence, these steps are preparing and enhancing the dataset by converting the order timestamps to datetime format and extracting the hour component for further analysis or insights related to the timing of the restaurant orders.

    Hourse data with items orders

    Hours_data=merged_df.pivot_table(index='item_name',columns='Hours',values='order_details_id',aggfunc='count')
print(Hours_data)
    • Create Pivot Table: Hours_data = merged_df.pivot_table(index=’item_name’, columns=’Hours’, values=’order_details_id’, aggfunc=’count’)
    • Uses the pivot_table function to organize data. ‘item_name’ becomes the index, ‘Hours’ becomes the columns, and counts of ‘order_details_id’ are the values.
    • Print the Pivot Table: print(Hours_data)
    • Displays the resulting pivot table.
      Shows counts of orders (‘order_details_id’) for each menu item (‘item_name’) at different hours (‘Hours’).
    hours data item wise orders

    CREATE HEATMAP ON ITEM AND ORDERS

    import seaborn as sns
    
plt.figure(figsize=(12,8))
    
sns.heatmap(heatmap_data, cmap='YlGnBu', annot=True, fmt='g')
    
plt.title("order detail heatmap by hours")
    
plt.show()
    • Import Libraries: import seaborn as sns: Imports the Seaborn library for statistical data visualization.
    • Set Figure Size: plt.figure(figsize=(12, 8)): Sets the size of the Matplotlib figure to be 12 units in width and 8 units in height.
    • Create Heatmap: sns.heatmap(heatmap_data, cmap=’YlGnBu’, annot=True, fmt=’g’): Generates a heatmap using Seaborn.
    • heatmap_data: The data to be visualized in the heatmap.
      cmap=’YlGnBu’: Specifies the color map for the heatmap (Yellow-Green-Blue). annot=True: Displays the numerical values in each cell of the heatmap.
      fmt=’g’: Specifies the format for the displayed values (general numeric format).
      Set Title:
    • plt.title(“order detail heatmap by hours”): Sets the title of the heatmap to “order detail heatmap by hours”.
      Show the Plot:
    • plt.show(): Displays the created heatmap.
      In summary, this code creates a heatmap using Seaborn to visualize the ‘heatmap_data’. The figure size is set, the color map is chosen, numerical values are annotated, and a title is added to enhance the clarity of the visualization. The resulting heatmap provides insights into order details at dImport Libraries:
    • import seaborn as sns: Imports the Seaborn library for statistical data visualization.
      Set Figure Size:
    • plt.figure(figsize=(12, 8)): Sets the size of the Matplotlib figure to be 12 units in width and 8 units in height.
      Create Heatmap:

    sns.heatmap(heatmap_data, cmap=’YlGnBu’, annot=True, fmt=’g’): Generates a heatmap using Seaborn.
    heatmap_data: The data to be visualized in the heatmap.
    cmap=’YlGnBu’: Specifies the color map for the heatmap (Yellow-Green-Blue).
    annot=True: Displays the numerical values in each cell of the heatmap.
    fmt=’g’: Specifies the format for the displayed values (general numeric format).
    Set Title:

    plt.title(“order detail heatmap by hours”): Sets the title of the heatmap to “order detail heatmap by hours”.
    Show the Plot:

    plt.show(): Displays the created heatmap.
    In summary, this code creates a heatmap using Seaborn to visualize the ‘heatmap_data’. The figure size is set, the color map is chosen, numerical values are annotated, and a title is added to enhance the clarity of the visualization. The resulting heatmap provides insights into order details at d

    HEATMAP
    Beginner's Guide Machine Learning and Data Science
    14Sep, 2023
    Beginner’s Guide: FAQs on Data Science and Machine Learning

    Machine learning and data science are related fields, but they have distinct focuses and objectives:

    Machine Learning (ML):

    Definition:

    Machine learning is a subset of artificial intelligence (AI) that involves the development of algorithms and models that allow computers to learn and make predictions or decisions based on data without being explicitly programmed.

    Goal:

    The primary goal of machine learning is to enable computers to recognize patterns, make predictions, or take actions based on data. It focuses on the development of algorithms and models that can improve their performance over time through learning from data.

    Techniques:

    Machine learning involves techniques such as supervised learning, unsupervised learning, and reinforcement learning. It often includes deep learning, which is a subfield of ML using neural networks.

    Applications:

    ML is widely used in various applications, including image and speech recognition, natural language processing, recommendation systems, autonomous vehicles, and more.

    Data Science:

    Definition:

    Data science is a multidisciplinary field that combines various techniques and methods to extract insights, knowledge, and value from data. It encompasses data analysis, data cleaning, data visualization, and the application of statistical and machine learning techniques.

    Goal:

    The primary goal of data science is to solve complex problems and make data-driven decisions by exploring and analyzing data. Data scientists often work on the entire data lifecycle, from data collection and cleaning to model building and deployment.

    Techniques:

    Data science includes a broader range of techniques than just machine learning. It involves data preprocessing, exploratory data analysis, statistical analysis, and data visualization, in addition to machine learning.

    Applications:

    Data science is applied across various domains, including business analytics, healthcare, finance, social sciences, and more. Data scientists help organizations make informed decisions and uncover hidden patterns in data

    Summary

    In summary, while machine learning is a subset of data science, data science encompasses a wider range of activities, including data collection, cleaning, exploration, visualization, and statistical analysis. Machine learning, on the other hand, specifically focuses on developing algorithms that allow computers to learn from data and make predictions or decisions. Both fields are crucial for extracting valuable insights and creating predictive models from data, and they often overlap in practice, with data scientists using machine learning techniques as part of their toolkit.

     

    In layman explanation Machine Learning and Data Science

    Machine Learning (ML):

    Imagine you have a pet dog, and you want to teach it a new trick, like catching a ball. At first, your dog doesn’t know how to do it. So, you show your dog how to catch the ball a few times. After practicing, your dog gets better and better at catching the ball all on its own. That’s a bit like what machine learning is.

    In machine learning, we teach computers to learn from examples, just like teaching your dog a trick. But instead of balls, we use data. For example, if we want a computer to tell whether a picture has a cat in it or not, we’ll show it lots of pictures with and without cats. The computer learns from these pictures and gets better at recognizing cats in new pictures.

    Machine learning helps computers make decisions and predictions based on what they’ve learned from data. Think of it as teaching a computer to be smarter by showing it lots of examples.

    Data Science:

    Data science is like being a detective in the world of information. Think of data as pieces of a puzzle. Imagine you have a big, messy jigsaw puzzle with thousands of pieces, and you want to put it together to see the whole picture.

    Data scientists are like puzzle solvers. They collect pieces of information, clean them up (remove any pieces that don’t fit), and organize them. Then, they use special tools and techniques to figure out what the complete picture looks like.

    Data science isn’t just about solving puzzles, though. It’s also about finding valuable information and making important decisions based on the data you have. For example, if you’re running a lemonade stand, a data scientist might help you figure out the best days to sell lemonade based on weather data and customer preferences.

    So, in simple terms, machine learning is like teaching computers to learn from examples, and data science is like solving puzzles with data to discover important things and make smart choices. Both of these fields help us use computers and data to do amazing things

    AspectMachine LearningData Science
    DefinitionSubset of AI focused on computer learning from dataMultidisciplinary field for data analysis & insight
    Main GoalTeach computers to make predictions from dataExtract insights, solve problems using data
    FocusDeveloping learning algorithmsData collection, cleaning, analysis, visualization
    TechniquesSupervised, unsupervised, reinforcement learningData preprocessing, EDA, statistics, visualization
    ApplicationsImage/speech recognition, NLP, recommendationBusiness analytics, healthcare, finance, etc.
    Tools/SoftwarePython libraries (e.g., TensorFlow, scikit-learn)Python, R, SQL, data visualization tools
    Example               Teaching a computer to recognize cats in photosanalyzing sales data to improve a store’s profits

    Remember, these fields often overlap, and data scientists frequently use machine learning techniques as part of their work to analyze data and make predictions.

    Similarities: Machine Learning and Data Science​

    Certainly! While machine learning and data science are distinct fields, they share several similarities:

    Data Utilization:

    Both machine learning and data science heavily rely on data. They use data as their primary raw material, whether it’s for training machine learning models or for conducting data analysis to derive insights.

    Mathematics and Statistics:

    Both fields involve mathematics and statistics. In machine learning, mathematical models are used to make predictions, while data science often employs statistical techniques to analyze data and draw conclusions.

    Programming Skills:

    Professionals in both fields need programming skills. Python is a common language used in both machine learning and data science for tasks such as data manipulation, model development, and data visualization.

    Data Preprocessing:

    Both machine learning and data science require data preprocessing. This involves cleaning, transforming, and preparing data for analysis or model training to ensure its quality and accuracy.

    Visualization:

    Data visualization is important in both fields. Visualizing data helps in understanding patterns, trends, and outliers, which is crucial for making informed decisions and presenting results effectively.

    Problem Solving:

    Both fields are problem-solving oriented. Machine learning focuses on solving specific problems through predictive modeling, while data science addresses broader data-related challenges and makes data-driven decisions to solve complex problems.

    Iterative Process:

    Both fields involve an iterative process. In machine learning, models are trained, evaluated, and refined iteratively to improve performance. Data science projects often follow an iterative cycle of data collection, exploration, analysis, and decision-making.

    Real-World Applications:

    Machine learning and data science find applications in various industries. They are used in areas such as healthcare, finance, marketing, and more to extract valuable insights and automate decision-making.

    Interdisciplinary Nature:

    Both fields are interdisciplinary. They draw from various domains, including computer science, mathematics, statistics, domain-specific knowledge, and business understanding to solve problems effectively.

    Continuous Learning:

    Professionals in both fields need to stay up-to-date with the latest developments, tools, and techniques. The fields are dynamic and evolve rapidly, requiring practitioners to engage in lifelong learning.

    In summary, machine learning and data science share commonalities in their reliance on data, use of mathematical and statistical methods, programming skills, data preprocessing, visualization, problem-solving, real-world applications, interdisciplinary nature, and the need for continuous learning and adaptation to emerging technologies.

    Data science is a multidisciplinary field that involves collecting, cleaning, analyzing, and interpreting data to extract valuable insights and support decision-making. It combines techniques from statistics, computer science, and domain-specific knowledge to make sense of data.

    Machine learning is a subset of artificial intelligence (AI) that focuses on developing algorithms and models that enable computers to learn from data and make predictions or decisions without being explicitly programmed.

    Data science encompasses a broader set of activities, including data collection, cleaning, and visualization, as well as statistical analysis and machine learning. Machine learning is a specific technique within data science that involves training models to make predictions based on data.

    Data science is used in various fields, including business analytics (e.g., market analysis), healthcare (e.g., disease prediction), finance (e.g., risk assessment), and social sciences (e.g., sentiment analysis on social media).

    Machine learning is used in applications like spam email filtering, recommendation systems (e.g., Netflix movie recommendations), self-driving cars, facial recognition, and natural language processing (e.g., chatbots and voice assistants).

    Data science projects typically involve stages like data collection, data preprocessing (cleaning and transforming), exploratory data analysis (EDA), model development, model evaluation, and results interpretation.

    Important skills include programming (e.g., Python or R), data manipulation, statistics, machine learning algorithms, data visualization, domain knowledge, and problem-solving abilities.

    Python is the most commonly used programming language, and popular libraries include TensorFlow, scikit-learn, and pandas. R is also used for data analysis. Tools like Jupyter notebooks and data visualization libraries (e.g., Matplotlib and Seaborn) are widely used.

    Yes, there is Vista Academy  courses, tutorials, and resources available  www.thevistaacademy.com

    The fields are expected to continue growing, with increasing applications in industries such as healthcare, finance, and technology. Advances in deep learning and AI are likely to drive further innovation.

    Supervised Machine Learning VS Unsupervised Machine Learning
    supervised and unsupervised machine learning:
    1Sep, 2023
    Supervised Machine Learning VS Unsupervised Machine Learning

    Supervised Machine Learning:

    Supervised machine learning. Supervised machine learning is a subfield of artificial intelligence (AI) and machine learning where the algorithm learns from labeled data to make predictions or classify new, unseen data. Here’s an overview of supervised machine learning:

    Imagine you’re teaching a child to recognize different animals. You show them pictures of cats, dogs, and horses, and you tell them which animal is in each picture. The child learns from your guidance and starts to recognize these animals on their own.

    In this scenario:

    • You, as the teacher, provide labeled data (telling them which animal is in each picture).
    • The child learns to make predictions (recognizing animals) based on the examples you’ve given.
    • This is similar to supervised machine learning.

    In supervised machine learning:

    • You have a dataset with labeled examples (input data with corresponding output or target labels).
    • The algorithm learns from these examples to make predictions or classifications when presented with new, unseen data.
    • Common tasks include image recognition (like the child recognizing animals), spam email classification, and predicting house prices based on features like size and location.

    Imagine you have a super-smart computer friend named AI-Buddy.

    AI-Buddy’s Superpower:

    It’s really good at learning from examples and making predictions.

    Here’s how AI-Buddy works:

    Step 1: Learning from Examples

    Imagine you have a bunch of pictures of different animals, and each picture comes with a label that tells you what animal is in it. For example, a picture of a cat is labeled “cat,” and a picture of a dog is labeled “dog.” These labels are like cheat sheets for AI-Buddy.

    So, you show all these pictures and labels to AI-Buddy. It looks at them and starts to notice patterns. It learns, “Oh, when I see pointy ears and whiskers, it’s usually a cat, and when I see floppy ears and a wagging tail, it’s usually a dog.”

    Step 2: Making Predictions

    Now, here’s the cool part. After AI-Buddy has learned from these examples, you can show it a new picture, one it has never seen before. You don’t tell AI-Buddy what’s in the picture; you keep it a secret. But AI-Buddy uses what it learned from the labeled pictures to guess what’s in the mystery picture.

    It might say, “Hmm, based on what I’ve learned, I think this new picture has a cat in it!” or “I think it’s a dog!”

    Step 3: Checking How Good It Is

    To make sure AI-Buddy is really good at guessing, you give it more mystery pictures with hidden labels. You see how many times it’s right and how many times it’s wrong. This helps you figure out if AI-Buddy is doing a great job or if it needs more practice.

    Some Special Jobs for AI-Buddy:

    Sorting Emails: You can use AI-Buddy to figure out which emails are spam (annoying) and which ones are not. It’s like having a personal email bouncer.

    Pricing Houses: If you want to sell your house, AI-Buddy can look at details like the size and location and tell you how much it’s worth. Handy for home sellers!

    Cool Facts:

    AI-Buddy can be trained to do all kinds of jobs, not just animals and houses. It can help doctors diagnose diseases, decide which movies to recommend, and even recognize your face to unlock your phone.

    Sometimes, AI-Buddy can get a bit too confident (like a student who memorizes answers but doesn’t understand). Or it can be too shy (like when it’s too scared to guess). So, we need to find the right balance to make it super smart.

    So, that’s supervised machine learning in a nutshell! It’s like having a smart friend who learns from examples and helps you make predictions. It’s used in lots of real-life things, making it easier for computers to understand the world around us.

     

    overview of supervised machine learning:

    Labeled Data:

    In supervised learning, you have a dataset that consists of input features and corresponding output labels. These labels provide the “ground truth” or correct answers, which the algorithm uses to learn and make predictions.

    Learning Objective:

    The primary goal of supervised learning is to learn a mapping or relationship between input features and output labels. This relationship is often represented by a mathematical model that can generalize from the training data to make predictions on new, unseen data.

    Examples of Supervised Learning Tasks:

    Classification:

    In classification tasks, the algorithm assigns data points to predefined categories or classes. For example, classifying emails as spam or not spam.


    Regression:

    In regression tasks, the algorithm predicts continuous numeric values. For instance, predicting house prices based on features like size and location.
    How Supervised Learning Works:

    The supervised learning process typically involves three main steps:

    • Training: The algorithm is exposed to the labeled training data, where it learns the relationships between input features and output labels.
    • Validation: A portion of the data (validation dataset) is used to fine-tune model hyperparameters and assess its performance before deployment.
    • Testing: The model’s performance is evaluated on a separate testing dataset to measure its ability to make accurate predictions on new, unseen data.
    • Evaluation Metrics: To assess the performance of a supervised learning model, various evaluation metrics are used, depending on the task. Common metrics include accuracy, precision, recall, F1-score (for classification), and mean squared error (MSE) or R-squared (for regression).

    Common Algorithms: There are numerous supervised learning algorithms, including:

    • Linear Regression: Used for regression tasks.
    • Logistic Regression: Commonly used for binary classification.
    • Decision Trees: Versatile for both classification and regression.
      Support Vector Machines (SVMs): Suitable for classification and regression.
    • Random Forest: An ensemble method for classification and regression.
    • Neural Networks: Deep learning models used for various tasks.
      Challenges:

    Common challenges in supervised learning include overfitting (when the model performs well on training data but poorly on new data) and underfitting (when the model is too simple to capture the data’s complexity).
    Applications: Supervised learning is applied in a wide range of fields, including healthcare (medical diagnosis), finance (credit scoring), natural language processing (text classification), and computer vision (image recognition).

    Supervised machine learning is a foundational concept in AI and data science, and it has enabled many real-world applications where making predictions or classifying data based on known patterns is crucial.

    Unsupervised Learning in Machine Learning

    Unsupervised Machine Learning:

    Unsupervised machine learning is another important subfield of artificial intelligence and machine learning. In unsupervised learning, algorithms work with unlabeled data to uncover hidden patterns, structures, or relationships within the data. Here’s an overview of unsupervised machine learning:

     

    Unsupervised machine learning is another important subfield of artificial intelligence and machine learning. In unsupervised learning, algorithms work with unlabeled data to uncover hidden patterns, structures, or relationships within the data. Here’s an overview of unsupervised machine learning:

    Now, let’s say you have a basket of assorted fruits, but you don’t know which fruits are similar or different. You ask the child to group the fruits based on their similarities without telling them the names of the fruits.

    In this case:

    • The child groups the fruits based on some hidden patterns or similarities they observe among them.
    • The child doesn’t have labels or prior information; they are exploring the data on their own.
    • This is similar to unsupervised machine learning.

    In unsupervised machine learning:

    • You have a dataset without labeled outcomes.
    • The algorithm tries to find patterns, similarities, or groupings in the data without any pre-existing knowledge.
    • Common tasks include clustering (grouping similar items), dimensionality reduction (simplifying complex data), and anomaly detection (finding unusual patterns in data).

    In summary, supervised machine learning is like teaching with labeled examples, while unsupervised machine learning is like discovering patterns and structures in data without any pre-existing information. Both approaches have their own uses and are valuable in different real-life applications.

    What Unsupervised Learning Does:

    Sorting Similar Things:

    One thing unsupervised learning can do is group similar things together. It’s like when you put all your toys in one box, your books on a shelf, and your clothes in a drawer without anyone telling you how to organize them.

    Simplifying Complicated Stuff:

    Imagine you have a really messy room with lots of different things scattered around. Unsupervised learning can help tidy up by finding ways to represent all those things with just a few key items, making it easier to understand.

    Spotting Odd Stuff:

    Let’s say you’re checking your piggy bank, and you notice that one coin doesn’t look like the others. Unsupervised learning can help you find those strange things, like a coin that doesn’t belong.

    How Unsupervised Learning Works:

    Think of unsupervised learning like a detective. It doesn’t have a list of suspects or clues; it has to find them itself. It uses math and patterns to organize, simplify, and spot unusual things in the data.

    When is Unsupervised Learning Useful:

    Unsupervised learning can be helpful when you want to organize a messy room, group similar things, or spot something unusual in a big pile of stuff. It’s like having a super-smart helper when you’re trying to make sense of things without a rulebook.

    Examples of Unsupervised Learning:

    Organizing Things: Imagine you have a collection of colorful marbles, and you want to group them by color. Unsupervised learning can do that without you telling it which colors are which.

    Cleaning Up Data:

    If you have a lot of messy data with lots of numbers, unsupervised learning can help simplify it, like turning a messy painting into a simple drawing.

    Detecting Strange Behavior:

    In a big crowd of people, unsupervised learning can help security teams find someone who’s acting strangely or doesn’t belong, just like a superhero spotting a hidden villain.

    Challenges of Unsupervised Learning:

    Sometimes, unsupervised learning might group things in a way that doesn’t make sense to us, or it might not find the odd things if they’re really tricky to spot. It’s like a detective who occasionally makes mistakes.

    Unsupervised learning is like a helpful detective for data, helping us organize, simplify, and spot unusual things in a way that makes complex data easier to understand.

    overview of unsupervised machine learning:

    Unlabeled Data:

    Unlike supervised learning, unsupervised learning deals with data that lacks predefined output labels. Instead, it focuses solely on the input features.

    Learning Objective:

    The primary goal of unsupervised learning is to discover and understand the inherent structure or organization within the data without relying on prior knowledge or guidance.

    Examples of Unsupervised Learning Tasks:

    • Clustering: Unsupervised algorithms group similar data points together. For instance, clustering customers based on their purchasing behavior.
    • Dimensionality Reduction: These algorithms reduce the number of input features while preserving important information. For example, simplifying a dataset with many attributes.
    • Anomaly Detection: Unsupervised methods can identify unusual or abnormal patterns in data, such as detecting fraudulent transactions in financial data.
    • Feature Learning: Techniques like autoencoders are used to automatically learn useful representations or features from raw data.

    How Unsupervised Learning Works:

    • Unsupervised learning generally involves exploring data without predefined labels or targets.
    • Algorithms analyze the data’s inherent structure, often using mathematical techniques like clustering, principal component analysis (PCA), or autoencoders.
    • The outcomes of unsupervised learning can include clusters of similar data points, reduced feature dimensions, or the identification of anomalies.
    • Evaluation Metrics: The choice of evaluation metrics in unsupervised learning depends on the specific task. For clustering, metrics like the silhouette score are used to measure the quality of clusters.

    Common Algorithms:

    • K-Means Clustering: Groups data points into clusters based on similarity.
      Hierarchical Clustering: Forms a hierarchy of clusters.
    • Principal Component Analysis (PCA): Reduces dimensionality by finding orthogonal axes capturing the most variance.
    • Autoencoders: Neural network-based models for feature learning.
      Isolation Forest: Anomaly detection algorithm.
      Applications:

    Unsupervised learning has applications in various domains, including marketing (customer segmentation), image compression, natural language processing (topic modeling), and network security (anomaly detection).
    Challenges:

    Unsupervised learning faces challenges such as determining the optimal number of clusters (in clustering tasks) and interpreting the discovered patterns.


    Use Cases:

    • Unsupervised learning can be used for exploratory data analysis, data preprocessing, and generating insights from large datasets.
    • Unsupervised machine learning is valuable for understanding data without prior labels or classifications. It helps uncover valuable insights and can be a critical step in the data analysis pipeline, enabling data scientists to make informed decisions based on the data’s inherent structure.

    Key differences between supervised and unsupervised learning

    Aspect

    Supervised Learning

    Unsupervised Learning

    Labeled Data Requirement

    Requires labeled data.

    Doesn’t require labeled data.

    Learning Objective

    Predict or classify based on labels.

    Discover data patterns/structures

    Teacher/Supervisor

    Requires a teacher/supervisor.

    Works without guidance.

    Output

    Generates predictions/classifications.

    Reveals hidden data structures.

    Common Applications

    Image recognition, sentiment analysis, recommendation systems.

    Clustering, dimensionality reduction, anomaly detection.

    Evaluation Metrics

    Accuracy, precision, recall, F1-score.

    Silhouette score, explained variance.

    Example Algorithms

    Linear Regression, Decision Trees

    K-Means Clustering, Principal Component Analysis.

    Feedback Loop

    Feedback loop for model improvement

    Limited feedback due to lack of labels.

    Data Preprocessing

    Often involves feature scaling, encoding.

    May involve preprocessing, less reliant on it.

    Validation Data Usage

    Common for tuning and validation.

    Less common, as no target labels.

     

     

     

    faq

    Supervised learning is a type of machine learning where an algorithm learns from labeled data to make predictions or classify new, unseen data.

    Labeled data consists of input features (data attributes) paired with corresponding output labels or target values. These labels provide the ground truth for training the model.

    Supervised learning algorithms analyze labeled data to identify patterns and relationships between input features and output labels. Once trained, the model can make predictions or classifications on new, unlabeled data.

    Supervised learning is used in various applications, including image recognition, natural language processing, recommendation systems, medical diagnosis, and financial forecasting.

    Common evaluation metrics include accuracy, precision, recall, F1-score (for classification tasks), and mean squared error (MSE) or R-squared (for regression tasks).

    in classification, the goal is to assign data points to predefined categories or classes. In regression, the goal is to predict continuous numeric values.

    Overfitting occurs when a model learns to perform exceptionally well on the training data but fails to generalize to new, unseen data. It often results from a model being too complex relative to the data.

    Underfitting happens when a model is too simple to capture the underlying patterns in the data. It performs poorly on both the training data and unseen data.

    A validation dataset is used to fine-tune model hyperparameters and assess its generalization performance before applying the model to real-world data. It helps prevent overfitting.

    One common example is the use of the Random Forest algorithm for classification tasks. It can be trained on labeled data to classify emails as spam or not based on various features.

    Unsupervised learning is a type of machine learning where algorithms work with unlabeled data to discover hidden patterns, structures, or relationships within the data

    The primary goal of unsupervised learning is to uncover the inherent structure or organization within data without having predefined output labels.

    Accordion Content

    In supervised learning, you have labeled data with input features and corresponding output labels, while unsupervised learning deals with unlabeled data and focuses solely on input features.

    Common tasks include clustering (grouping similar data points together), dimensionality reduction (simplifying data by reducing the number of features), anomaly detection (finding unusual patterns), and feature learning (automatically extracting useful features from data).

    Think of unsupervised learning as a detective who explores a jigsaw puzzle without a picture on the box. The detective tries to group similar puzzle pieces and discover any unusual ones to solve the puzzle.

    Unsupervised learning algorithms use mathematical techniques to analyze data and identify patterns. For example, clustering algorithms group similar data points, and dimensionality reduction techniques simplify complex data.

    The choice of evaluation metrics depends on the specific task. For clustering, metrics like silhouette score measure the quality of clusters. For dimensionality reduction,

    Common algorithms include K-Means Clustering, Hierarchical Clustering, Principal Component Analysis (PCA), Autoencoders, and Isolation Forest for anomaly detection.

    Unsupervised learning is used in various domains, including customer segmentation in marketing, image compression in media, topic modeling in natural language processing, and network security for anomaly detection.

    Challenges include determining the optimal number of clusters in clustering tasks, interpreting the discovered patterns, and handling noisy or complex data.

    nsupervised learning is useful when you want to explore data without prior labels, organize data into meaningful groups, simplify complex data, or detect unusual patterns in large datasets.

    Top 10 Example of machine learning for Data Analytics
    Comprehensive Analysis of Airline Passenger Satisfaction in python
    20Aug, 2023
    Comprehensive Analysis of Airline Passenger Satisfaction in python

    In today’s ever-connected world, air travel serves as a vital conduit, linking people and places across the globe. Delving into the realm of airline passenger satisfaction, our analysis unearths the intricate threads that shape travelers’ experiences in the skies. By dissecting data encompassing diverse parameters such as customer type, travel preferences, and class, we uncover insights that offer airlines a compass to enhance their services. Embark on this journey with us to decode the nuances of passenger contentment and pave the way for elevated travel experiences.

    DataSet Link

    Sure, here’s a concise summary of the dataset in bullet points:

    Dataset Overview:

    • Contains 129,880 entries, offering a substantial dataset for analysis.
    • Encompasses 24 columns, each providing distinct insights into passenger experiences.
      Demographic Details:
    • Captures key demographics such as gender, age, and customer type.
      Provides a foundation for understanding the passenger profile.


    Travel Specifics:

    • Includes variables like flight distance and departure delays.
    • Offers insight into the logistical aspects of the journey.
    • Flight Experience Categories:

    Delves into various aspects of the flight experience:

    • Ease of online booking, check-in service, online boarding, gate location.
    • On-board service, seat comfort, leg room service, cleanliness.
    • Food and drink, in-flight service, in-flight WiFi, in-flight entertainment.
    • Baggage handling.
      Explores passenger perceptions of different service aspects.


    Satisfaction Measurement:

    • Evaluates passenger satisfaction through the “Satisfaction” column.
    • Gauges passenger contentment based on provided services.


    Comprehensive Insights:

    • The dataset covers a wide range of factors influencing the flying experience.
      Enables detailed analysis of both positive and negative aspects of air travel.


    Potential Analysis:

    • Allows for in-depth exploration of correlations between variables.
      Offers an opportunity to understand factors contributing to passenger satisfaction.


    Key Takeaways:

    • A rich dataset offering a comprehensive view of airline passenger experiences.
    • Provides a platform to unravel the dynamics of passenger satisfaction across diverse dimensions.
    • This dataset promises a deep dive into the realm of airline satisfaction, presenting a unique opportunity to glean actionable insights for enhancing the passenger journey.

     

    Import Libraries in Jupyter Notebook

    import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
    • pandas (imported as pd): Pandas is a powerful library for data manipulation and analysis. It provides data structures like DataFrames and Series that allow you to work with structured data efficiently.
    • numpy (imported as np): NumPy is a library for numerical computations in Python. It provides support for large, multi-dimensional arrays and matrices, along with mathematical functions to operate on these arrays.
    • matplotlib.pyplot (imported as plt): Matplotlib is a widely used library for creating static, interactive, and animated visualizations in Python. The pyplot module within Matplotlib provides a MATLAB-like interface for generating plots and charts.
    • seaborn (imported as sns): Seaborn is built on top of Matplotlib and provides a higher-level interface for creating informative and attractive statistical graphics. It simplifies the process of creating complex visualizations and comes with several built-in themes and color palettes.
    Exploring the seaborn Library:

    Seaborn is particularly useful for statistical data visualization. It provides a variety of functions to create visually appealing plots with less code compared to using Matplotlib directly. Seaborn’s features include:

    • High-Level Functions: Seaborn offers functions to create common types of statistical plots such as scatter plots, bar plots, histograms, box plots, etc. These functions often take a DataFrame or Series as input and handle much of the underlying plotting logic automatically.
    • Enhanced Aesthetics: Seaborn provides a set of built-in themes and color palettes that make your plots more visually appealing by default. This reduces the need for manual customization.
    • Statistical Estimations: Many Seaborn plots include options for adding statistical estimations to the visualization, such as regression lines on scatter plots or confidence intervals on bar plots.
    • Categorical Plotting: Seaborn simplifies working with categorical data by providing functions for creating plots that summarize and compare data within different categories.
    • Overall, Seaborn complements Matplotlib by simplifying the process of creating complex visualizations, making it a popular choice for data analysts and scientists.

    Please note that while the code snippet imports the libraries, it doesn’t include any actual code for data manipulation or visualization. You would typically write additional code to load data using Pandas, perform data analysis or transformations using both Pandas and NumPy, and then use Seaborn (and Matplotlib) to create visualizations based on the analyzed data.

    Read Csv File

    data = pd.read_csv(r"C:\Users\yogesh\Downloads\Airline+Passenger+Satisfaction\airline.csv")
    import pandas as pd:

    This imports the Pandas library and assigns it the alias pd. This is a common convention to make the code shorter when referring to Pandas functions.

    data = pd.read_csv(r”C:\Users\yogesh\Downloads\Airline+Passenger+Satisfaction\airline.csv”): Here’s what each part of this line does:

    data:

    This is a variable name that you’re using to store the data read from the CSV file. You can choose any variable name you prefer.

    • pd.read_csv(): This is a Pandas function that reads a CSV file and returns its content as a DataFrame.
    • r”C:\Users\yogesh\Downloads\Airline+Passenger+Satisfaction\airline.csv”: This is the path to the CSV file that you want to read. The r before the string indicates that it’s a “raw” string, which means backslashes are treated as literal characters and not escape characters. Adjust the path to match the location of your actual “airline.csv” file.
    • Once the code is executed, the CSV file’s contents will be loaded into the data DataFrame.

    print(data.head())

    The print(data.head()) command is used to display the first few rows of the DataFrame data. The .head() method in Pandas returns the top rows of a DataFrame, by default the first 5 rows.
    The print(data.head()) command is used to display the first few rows of the DataFrame data. The .head() method in Pandas returns the top rows of a DataFrame, by default the first 5 rows.
    head output pandas

    Display basic info about the dataset

    print(data.info())
    • The information displayed typically includes:
    • The total number of non-null entries in each column.
    • The data type of each column (e.g., integer, float, string, datetime, etc.).
    • The memory usage of the DataFrame.
    • The index information.
      Additional metadata about the DataFrame.

    The describe() function

    print(data.describe())

    When applied to a Pandas DataFrame or Series, the describe() function provides a summary of descriptive statistics for the data. These statistics include measures such as mean, standard deviation, minimum, maximum, and quartiles for numerical data, as well as count and unique values for categorical data.

    Isnull Function in Pandas for Data analytics

    #Check for missing values
print(data.isnull().sum())

    when you run print(data.isnull().sum()), it will print the count of missing values in each column of your DataFrame. In below out put arrival delay have 393 missing values.

    fillna function for filling values in Pandas

    # Assuming 'data' is your DataFrame
data['Arrival Delay'] = data['Arrival Delay'].fillna(0)

# Print to verify the changes
print(data.isnull().sum())

    Fill 0 in missing value in dataframe.

    fill na in pandas

    Charting in Python

    # Define the categories of columns
categories = ['Ease of Online Booking', 'Check-in Service', 'Online Boarding',
              'Gate Location', 'On-board Service', 'Seat Comfort',
              'Leg Room Service', 'Cleanliness', 'Food and Drink',
              'In-flight Service', 'In-flight Wifi Service', 'In-flight Entertainment']

# Calculate the averages for each category
category_averages = data[categories].mean()

# Create a bar chart
plt.figure(figsize=(10, 6))
category_averages.plot(kind='bar', color='orange')
plt.title('Average Ratings for Flight Experience Categories')
plt.xlabel('Categories')
plt.ylabel('Average Rating')
plt.xticks(rotation=45)
plt.tight_layout()

# Show the chart
plt.show()

    Column Categories Definition:


    A list called categories is defined, containing the names of different categories that are likely related to the evaluation of a flight experience. These categories might include things like ease of online booking, check-in service, seat comfort, in-flight entertainment, etc.

    Calculating Category Averages:


    The code assumes the existence of a DataFrame named data, which presumably contains ratings or scores for each of the specified categories. The mean() function is used to calculate the average value for each category based on the ratings provided in the DataFrame.

    Creating a Bar Chart:


    The code uses the matplotlib library to create a bar chart. It sets the figure size to 10×6 inches with plt.figure(figsize=(10, 6)). Then, it plots the average ratings using the category_averages Series as data, and sets the bars to be colored orange (color=’orange’). The kind parameter is set to ‘bar’ to indicate that a bar chart should be created.

    Chart Title and Labels:


    Various attributes are set to customize the appearance of the chart. The plt.title() function sets the chart title to ‘Average Ratings for Flight Experience Categories’. The plt.xlabel() and plt.ylabel() functions set the labels for the x-axis and y-axis respectively. The plt.xticks(rotation=45) function rotates the x-axis labels by 45 degrees for better readability.

    Adjusting Layout and Displaying:


    The plt.tight_layout() function is called to ensure that the plot elements do not overlap. Finally, plt.show() is called to display the generated bar chart.

    In summary, this code takes ratings or scores for different flight experience categories from a DataFrame, calculates the average ratings for each category, and then creates and displays a bar chart to visually represent these average ratings. The chart provides insights into which categories of the flight experience are rated more favorably or less favorably on average.

     

    average rating of flight chart

    Create a line plot

     #Create a line plot
plt.figure(figsize=(10, 6))
category_averages.plot(kind='line', marker='o', color='blue')
plt.title('Average Ratings for Flight Experience Categories')
plt.xlabel('Categories')
plt.ylabel('Average Rating')
plt.xticks(rotation=45)
plt.tight_layout()

# Show the chart
plt.show()
    • plt.figure(figsize=(10, 6)): This line creates a new figure (plotting area) with a specified size of 10 inches in width and 6 inches in height. This sets up the canvas on which the plot will be drawn.
    • category_averages.plot(kind=’line’, marker=’o’, color=’blue’): This line is creating the line plot itself. It’s assumed that category_averages is a DataFrame or a Series containing data for the plot. Here’s what each parameter does:
    • kind=’line’: Specifies that a line plot should be created.
      marker=’o’: Sets circular markers at data points on the line.
      color=’blue’: Sets the color of the line and markers to blue.
      plt.title(‘Average Ratings for Flight Experience Categories’): This sets the title of the plot to “Average Ratings for Flight Experience Categories”.
    • plt.xlabel(‘Categories’): This sets the label for the x-axis to “Categories”.
    • plt.ylabel(‘Average Rating’): This sets the label for the y-axis to “Average Rating”.
    • plt.xticks(rotation=45): This rotates the x-axis labels by 45 degrees. This is often done to prevent overlapping when labels are long or numerous.
    • plt.tight_layout(): This function adjusts the spacing between the plot elements to ensure they fit well within the figure area.
    • plt.show(): This line displays the plot on the screen.

    In summary, the code creates a line plot that displays average ratings for different flight experience categories. The categories are labeled on the x-axis, the average ratings are on the y-axis, and each data point is marked with a blue circular marker. The plot has a title and axis labels, and the x-axis labels are rotated for better readability. Finally, the plot is displayed using plt.show().

    Create a histogram

    #Create a histogram for the "Age" column
plt.figure(figsize=(10, 6))
plt.hist(data['Age'], bins=20, color='skyblue', edgecolor='black')
plt.title('Age Distribution')
plt.xlabel('Age')
plt.ylabel('Frequency')
plt.tight_layout()

# Show the histogram
plt.show()
    The plt.figure(figsize=(10, 6)) line sets the dimensions of the plot. The plt.hist() function is used to create the histogram. It takes the ‘Age’ column data from the dataset (data[‘Age’]), divides it into 20 bins, and represents the data using bars in the plot. The bars are colored in sky blue and have black edges. The plt.title() function sets the title of the plot to “Age Distribution,” while plt.xlabel() and plt.ylabel() set the labels for the x-axis and y-axis respectively. The plt.tight_layout() function adjusts the spacing of the plot elements for better layout. Finally, plt.show() is called to display the histogram plot. The histogram provides a visual representation of how the ages are distributed within the dataset, showing the frequency of ages in different bins.

    Age Groups to the Dataset:

    # Define labels for the age bins
age_labels = ['0-18', '19-30', '31-40', '41-50', '51-60', '61-70', '71+']

# Add a new column with age groups
data['Age Group'] = pd.cut(data['Age'], bins=7, labels=age_labels)

# Display the modified data with age groups
print(data['Age Group'])
    • age_labels: This is a list of labels representing different age bins or groups. Each label corresponds to a specific age range.
    • data[‘Age Group’] = pd.cut(data[‘Age’], bins=7, labels=age_labels): This line of code adds a new column called ‘Age Group’ to the DataFrame data.
    • The pd.cut() function is used to categorize the values in the ‘Age’ column into 7 bins (or age groups) based on the specified labels and number of bins. The resulting categories are assigned the labels from the age_labels list.
    • print(data[‘Age Group’]): This line prints out the newly created ‘Age Group’ column, displaying the age group label for each row of data.

    In summary, the code takes an existing DataFrame with an ‘Age’ column, divides the age values into 7 different age groups, assigns corresponding labels to these groups, and then adds a new ‘Age Group’ column to the DataFrame to store these group labels. This can be useful for segmenting and analyzing data based on different age ranges.

    age group in pandas airline data

    Passenger Distribution by Age Group

    age_group_counts = data['Age Group'].value_counts().sort_index()
#Create a bar chart
plt.figure(figsize=(10, 6))
age_group_counts.plot(kind='bar', color='skyblue')
plt.title('Passenger Distribution by Age Group')
plt.xlabel('Age Group')
plt.ylabel('Number of Passengers')
plt.xticks(rotation=0)
plt.tight_layout()

# Show the chart
plt.show()

    age_group_counts = data[‘Age Group’].value_counts().sort_index():

    This line calculates the count of passengers in each age group using the ‘Age Group’ column of the DataFrame. The .value_counts() function counts the occurrences of each unique value (in this case, the different age group labels), and .sort_index() sorts these counts based on the order of the age labels.

    Creating the Bar Chart:

    • plt.figure(figsize=(10, 6)): This line sets the size of the figure (the plot) to have dimensions of 10 units in width and 6 units in height.
    • age_group_counts.plot(kind=’bar’, color=’skyblue’):
    • This line creates a bar plot using the counts calculated in the previous step. Each age group will have a corresponding bar, and the bars will be colored with a sky blue color.
    • plt.title(‘Passenger Distribution by Age Group’): Sets the title of the plot to ‘Passenger Distribution by Age Group’.
    • plt.xlabel(‘Age Group’): Sets the label for the x-axis to ‘Age Group’.
    • plt.ylabel(‘Number of Passengers’): Sets the label for the y-axis to ‘Number of Passengers’.
      plt.xticks(rotation=0): Sets the rotation angle of the x-axis labels to 0 degrees, meaning the labels will be displayed horizontally.
    • plt.tight_layout(): Automatically adjusts the subplot parameters to ensure that all elements of the plot fit within the figure area without overlapping.
    age group wise distribution of data in airline passenger satisfaction report in python

    Create Pie Chart

    gender_counts = data['Gender'].value_counts() customer_type_counts = data['Customer Type'].value_counts() travel_type_counts = data['Type of Travel'].value_counts() class_counts = data['Class'].value_counts()
    Here, you’re calculating the count of unique values in the ‘Gender’, ‘Customer Type’, ‘Type of Travel’, and ‘Class’ columns of the DataFrame data and storing them in separate variables.

    Creating Subplots:

    fig, axes = plt.subplots(2, 2, figsize=(12, 10))
    You’re creating a 2×2 grid of subplots (four subplots in total) using the subplots function from the matplotlib.pyplot library. The figsize parameter determines the size of the entire figure.

    Pie Chart Creation

      
    axes[0, 0].pie(gender_counts, labels=gender_counts.index, autopct='%1.1f%%', startangle=90)
axes[0, 1].pie(customer_type_counts, labels=customer_type_counts.index, autopct='%1.1f%%', startangle=90)
axes[1, 0].pie(travel_type_counts, labels=travel_type_counts.index, autopct='%1.1f%%', startangle=90)
axes[1, 1].pie(class_counts, labels=class_counts.index, autopct='%1.1f%%', startangle=90)
    You’re creating individual pie charts for each categorical variable. For each subplot, you’re using the pie function to create the pie chart. The labels parameter specifies the labels for each slice of the pie chart, the autopct parameter formats the percentage labels on the chart, and startangle determines the angle at which the first slice starts.

    Setting Titles:

      
    axes[0, 0].set_title('Gender Distribution')
axes[0, 1].set_title('Customer Type Distribution')
axes[1, 0].set_title('Type of Travel Distribution')
axes[1, 1].set_title('Class Distribution')

    Layout Adjustment and Displaying: 

    plt.tight_layout()
plt.show()
    plt.tight_layout() adjusts the spacing between subplots for better visualization. Finally, plt.show() displays the entire figure with the pie charts.
    pie chart creation in pandas for data analytics on airline passenger satisfaction data

    bar chart of satisfaction Mapping

    satisfaction_mapping = {
    'Neutral or Dissatisfied': 0,
    'Satisfied': 1
}
    In this step, a dictionary named satisfaction_mapping is created. It maps categorical satisfaction values (‘Neutral or Dissatisfied’ and ‘Satisfied’) to numerical values (0 and 1, respectively).

    Step 2: Map the Satisfaction Values

    data['Satisfaction'] = data['Satisfaction'].map(satisfaction_mapping)
    the ‘Satisfaction’ column in the DataFrame named data by applying the mapping defined earlier. This maps the categorical values to their corresponding numerical values in the DataFrame.

    Step 3: Calculate Average Satisfaction for Each Class

    class_satisfaction = data.groupby('Class')['Satisfaction'].mean()
    This line groups the data in the DataFrame by the ‘Class’ column and calculates the mean (average) of the ‘Satisfaction’ values for each class. The result is stored in the class_satisfaction Series.

    Step 4: Create a Bar Chart

    plt.figure(figsize=(10, 6))
class_satisfaction.plot(kind='bar', color='skyblue')
    In this step, a bar chart is created using the matplotlib library. The plt.figure(figsize=(10, 6)) sets the size of the figure, and class_satisfaction.plot(kind=’bar’, color=’skyblue’) plots the average satisfaction values for each class as bars, using the color ‘skyblue’.

    Step 5: Add Chart Details

    plt.title('Average Satisfaction by Class')
plt.xlabel('Class')
plt.ylabel('Average Satisfaction')
plt.xticks(rotation=0)
plt.tight_layout()
    These lines add various details to the chart:
    • plt.title(‘Average Satisfaction by Class’): Sets the chart title.
    • plt.xlabel(‘Class’): Labels the x-axis.
    • plt.ylabel(‘Average Satisfaction’): Labels the y-axis.
    • plt.xticks(rotation=0): Sets the rotation angle of the x-axis labels to 0 degrees (no rotation). plt.tight_layout(): Adjusts the layout of the plot to make sure everything fits nicely

    Step 6: Show the Chart

    plt.show()
    average satisfaction by class in python airline satisfaction

    Bar chart Average satisfaction for each type of travel

    # Calculate the average satisfaction for each type of travel
travel_satisfaction = data.groupby('Type of Travel')['Satisfaction'].mean()

# Create a bar chart
plt.figure(figsize=(10, 6))
travel_satisfaction.plot(kind='bar', color='skyblue')
plt.title('Average Satisfaction by Type of Travel')
plt.xlabel('Type of Travel')
plt.ylabel('Average Satisfaction')
plt.xticks(rotation=0)
plt.tight_layout()

# Show the chart
plt.show()

    Bar Chart by average satisfaction for each customer type

    # Calculate the average satisfaction for each customer type
customer_type_satisfaction = data.groupby('Customer Type')['Satisfaction'].mean()

# Create a bar chart
plt.figure(figsize=(10, 6))
customer_type_satisfaction.plot(kind='bar', color='skyblue')
plt.title('Average Satisfaction by Customer Type')
plt.xlabel('Customer Type')
plt.ylabel('Average Satisfaction')
plt.xticks(rotation=0)
plt.tight_layout()

# Show the chart
plt.show()
    Data Analytics: A Step-by-Step Guide for Beginners
    18Aug, 2023
    Navigating Data Analytics A Step-by-Step Guide for Beginners Hindi

    Introduction to Data Analytics in hindi

    डेटा एनालिटिक्स का परिचय”

    डेटा एनालिटिक्स एक प्रक्रिया है जिसका उपयोग डेटा से जानकारी और अनुभव प्राप्त करने के लिए किया जाता है। यह विभिन्न डेटा सेट्स का विश्लेषण करके पैटर्न, trends और insights पाने का तरीका है, जिससे व्यवसायिक और व्यक्तिगत निर्णय लिए जा सकते हैं। डेटा एनालिटिक्स के माध्यम से, आप डेटा को समझकर उसके पीछे की अर्थपूर्णता को खोज सकते हैं और उसे सकारात्मक तरीके से सेवन करने के लिए निर्णय ले सकते हैं।

    डेटा एनालिटिक्स का मुख्य उद्देश्य व्यक्तिगत, संगठनात्मक, और व्यावसायिक स्तरों पर सही निर्णय लेने में मदद करना है। यह डेटा से अद्भुत प्रतिष्ठित और गुप्त जानकारी निकालने का एक माध्यम भी है, जिससे विभिन्न क्षेत्रों में सुरक्षा, विपणन, वित्त, स्वास्थ्य, और अन्य क्षेत्रों में नये और सुधारित उत्पाद और सेवाएँ प्राप्त की जा सकती हैं।

    डेटा एनालिटिक्स में विभिन्न तरीके शामिल होते हैं, जैसे कि डेटा कलेक्शन, डेटा प्रसंस्करण, डेटा विश्लेषण, डेटा विज़ुअलाइज़ेशन, सांख्यिकीय विश्लेषण, मशीन लर्निंग, और अन्य। ये तरीके आपको डेटा से मानव बुद्धि से समझकर उसका अध्ययन करने में मदद करते हैं और नए और अद्भुत जानकारी का पता लगाने में सहायक होते हैं।

    डेटा एनालिटिक्स का उपयोग समय के साथ बढ़ते हुए डेटा के भंडारण, प्रसंस्करण, और विश्लेषण के क्षेत्र में हो रहे विकास को समझने में भी किया जाता है। यह निर्णय लेने में मदद करता है और आपको विभिन्न संदर्भों में सटीक और उपयुक्त निर्णय लेने में सहायता प्रदान करता है।

    कृपया ध्यान दें कि यह केवल एक पारिभाषिक अनुभाग है और आपके इच्छित पाठ्यक्रम के आधार पर विषय को गहराई से समझने के लिए आपको अधिक अध्ययन की आवश्यकता होगी।

    डेटा एनालिटिक्स कदमों की प्रक्रिया को समझने के लिए यहाँ एक सरल और सुलभ तरीका प्रस्तुत किया गया है

    कदम 1: समझाने का मकसद Understanding the Purpose of Data Analytics

    डेटा एनालिटिक्स के मकसद को समझने का यह लेख आपको एक रोचक उदाहरण के साथ बताएगा कि यह आपके व्यवसाय में कैसे महत्वपूर्ण हो सकता है।

    कदम 1: उद्देश्य की समझ: डेटा एनालिटिक्स की शुरुआत उद्देश्य की समझ से होती है। विचार करें, आपका व्यवसाय ई-कॉमर्स के क्षेत्र में है और आपका मुख्य उद्देश्य है कि आपके ग्राहक किस प्रकार के उत्पादों की खोज कर रहे हैं। आपका मकसद यह जानना है कि कौनसे उत्पाद आम तौर पर खरीदे जा रहे हैं और इस आधार पर आपको अपने इन्वेंटरी को प्रबंधित करने के फैसले लेने में मदद मिले।

    उदाहरण:Ecommerce Website

    समझाने के लिए, चलिए यह मान लें कि आपके पास एक ई-कॉमर्स वेबसाइट है जिसमें विभिन्न उत्पादों की बड़ी वस्तुए है। आपको डेटा एनालिटिक्स का उपयोग करके उन वार्ड्रेस की खोज करनी है जिनकी बिक्री अधिक हो रही है। इससे आपको यह समझने में मदद मिलेगी कि कौनसे वस्तुए प्रसिद्ध हो रहे हैं और आप उन्हें अधिक प्रोत्साहित करने के लिए योजना बना सकते हैं। यह आपकी बिक्री में वृद्धि का कारण बन सकता है।

    संवाद:

    इस तरीके से, डेटा एनालिटिक्स आपको व्यवसायिक निर्णयों में मदद कर सकता है जिससे आप अपने उद्देश्यों की प्राप्ति में मदद कर सकते हैं। जब आप उदाहरण के साथ इस प्रक्रिया को समझते हैं, तो यह आपको अधिक संलग्न करता है और सीखने को उत्साहित करता है।

    कदम 2: डेटा संग्रहण Data Collection

    डेटा एनालिटिक्स की यात्रा में कदम 2 बहुत महत्वपूर्ण होता है – डेटा संग्रहण। इस लेख में, हम वास्तविक जीवन से संबंधित उदाहरणों के साथ डेटा संग्रहण के महत्व की खोज करेंगे, जिससे यह महत्वपूर्ण प्रक्रिया समझने में और आपसी सहमति में आ सके।

    डेटा संग्रहण का महत्व:Importance of Data Collection

    डेटा संग्रहण एक महत्वपूर्ण कार्य है जहाँ आप भविष्य के विश्लेषण के लिए आवश्यक डेटा एकत्र करते हैं। यह डेटा एनालिटिक्स का मूल आधार बनता है, क्योंकि इसमें मूल्यवान जानकारी एकत्र की जाती है जो आपको सुचित निर्णय लेने में मदद करती है।

    उदाहरण: स्मार्ट ऊर्जा संवाद: Smart Energy Consumption:

    इसे समझने के लिए, एक उदाहरण की खोज करते हैं। सोचिए कि आप स्मार्ट होम तकनीक में ऊर्जा की उपयोगिता को अधिकतम करने के लिए डेटा एनालिटिक्स का इस्तेमाल कर रहे हैं। आप ऊर्जा उपयोग के पैटर्न, तापमान परिवर्तन और उपयोगकर्ता के व्यवहार पर डेटा एकत्र करते हैं। आपका उद्देश्य है कि आप ऐसे पैटर्न्स की पहचान करें जो उच्चतम कुशलता के लिए ऊर्जा का उपयोग स्वतंत्रता से करने में मदद कर सकें।

    उदाहरण: ऑनलाइन खरीददारी की सफलता:Online Retail Success:

    एक और परिदृश्य में सोचें जहाँ आप एक ई-कॉमर्स व्यवसाय हैं जो ग्राहक अनुभव को बेहतर बनाने का उद्देश्य रखता है। ग्राहकों के ब्राउज़िंग और खरीददारी व्यवहार का विश्लेषण करके, आप उनकी पसंद, अक्सर देखे जाने वाले उत्पाद और पिछली खरीददारियों पर डेटा एकत्र करते हैं। यह डेटा आपको व्यक्तिगत सुझावों को व्यक्त करने में मदद करता है, ग्राहकों की सहयोग और बिक्री में वृद्धि करने में मदद करता है।

    निष्कर्ष:conclusion

    ये उदाहरण दिखाते हैं कि डेटा संग्रहण कैसे विशिष्ट उद्देश्यों की प्राप्ति में महत्वपूर्ण भूमिका निभाता है। चाहे यह ऊर्जा उपयोग को सुचारू करना हो

    कदम 3: डेटा सफाई और अनुकूलन :Step 3: Data Cleaning and Preprocessing

    डेटा एनालिटिक्स में कदम 3 का महत्वपूर्ण होता है – डेटा सफाई और पूर्वसंस्करण। इस लेख में, हम वास्तविक उदाहरणों के साथ डेटा सफाई और पूर्वसंस्करण की महत्वपूर्णता को समझेंगे, जो यह प्रक्रिया समझने में मदद करेगा और रुचिकर बनाएगा।

    डेटा सफाई और पूर्वसंस्करण का महत्व:Importance

    डेटा सफाई और पूर्वसंस्करण उन चरणों में शामिल होते हैं जहाँ आप डेटा को साफ और उपयोगी बनाने की प्रक्रियाओं को अपनाते हैं। यह डेटा के अनशुद्धियों को दूर करता है और उसे विशेषज्ञता से तैयार करता है ताकि आगामी विश्लेषण में उपयोगी नतीजे प्राप्त किए जा सकें।

    उदाहरण: विमान यातायात की डेटा सफाई:AirlinePassenger data cleaning

    इसकी समझ के लिए, एक उदाहरण की ओर बढ़ते हैं। सोचें कि आप एक विमान यातायात कंपनी के डेटा विश्लेषण में शामिल हैं। आपने उनके यात्री डेटा को देखा और देखा कि कुछ डेटा अनशुद्धियों से प्रभावित हो रहा है, जैसे कि मिसिंग डेटा या गलत इनपुट। आपका उद्देश्य है कि आप उन डेटा अनशुद्धियों को सुधारें और उन्हें साफ और तैयार करें ताकि आप यात्री के प्रवास की स्थिति को सही से विश्लेषण कर सकें।

    निष्कर्ष:Conclusion

    ये उदाहरण दिखाते हैं कि डेटा सफाई और पूर्वसंस्करण कैसे विशेष उद्देश्यों की प्राप्ति में महत्वपूर्ण भूमिका निभाते हैं। आपके पास उदाहरणों से यह समझने में मदद मिलती है कि डेटा को कैसे साफ किया जाता है और उसे तैयार किया जाता है ताकि आगामी विश्लेषण में उपयोगी नतीजे प्राप्त किए जा सकें।

    कदम 4: डेटा विश्लेषण: Data Analysis Demystified

    डेटा एनालिटिक्स में कदम 4 का महत्वपूर्ण होता है – डेटा विश्लेषण। इस लेख में, हम उदाहरण के साथ डेटा विश्लेषण की महत्वपूर्णता को समझने का प्रयास करेंगे, जिससे आपको यह प्रक्रिया अधिक समझ में आएगी।

    डेटा विश्लेषण का महत्व:

    डेटा विश्लेषण उन प्रक्रियाओं का हिस्सा होता है जिनमें आप डेटा को विभिन्न तरीकों से विश्लेषित करते हैं ताकि आप उससे नए और महत्वपूर्ण ज्ञान प्राप्त कर सकें। यह आपको अद्यतित निर्णय लेने में मदद करता है और निर्धारित लक्ष्यों की प्राप्ति में सहायक होता है।

    उदाहरण:

    बिक्री प्रवृत्तियों का विश्लेषण: एक उदाहरण की मदद से समझते हैं। सोचें कि आप एक रिटेल व्यवसाय के मालिक हैं और आपका उद्देश्य है विक्रेता प्रवृत्तियों का विश्लेषण करके बेहतर समझना। आपने डेटा विश्लेषण के माध्यम से बिक्री के दौरान ग्राहकों के व्यवहार का अध्ययन किया। इससे आपको यह समझने में मदद मिलती है कि कौनसे उत्पादों की ज्यादा डिमांड होती है और किन प्रवृत्तियों का अनुसरण किया जा सकता है। आप इस जानकारी का उपयोग बिक्री स्ट्रैटेजी में सुधार करने के लिए कर सकते हैं।

    निष्कर्ष:

    उपरोक्त उदाहरण से हमने देखा कि डेटा विश्लेषण कैसे नए ज्ञान प्राप्त करने में मदद कर सकता है। यह आपको विशिष्ट उद्देश्यों की प्राप्ति में सहायक होता है और आपको अधिक सुरक्षित निर्णय लेने में मदद करता है।

     

    कदम 5: परिणामों का प्रस्तुतीकरण Presenting Results Effectively

    डेटा एनालिटिक्स के पांचवें कदम में हम जानेंगे कि परिणामों को कैसे प्रभावी तरीके से प्रस्तुत किया जाता है। इस लेख में, हम उदाहरण के साथ इस कदम की महत्वपूर्णता को समझेंगे और जानेंगे कि आपको अपने डेटा विश्लेषण के परिणामों को कैसे सरल और सुदृढ़ ढंग से प्रस्तुत करना चाहिए।

    परिणामों का प्रस्तुतीकरण का महत्व:Importance Presenting Results

    परिणामों का प्रस्तुतीकरण डेटा एनालिटिक्स की अंतिम प्रक्रिया होती है जो आपके विश्लेषण के परिणामों को दुनिया के सामने रखती है। यह आपको अपने निर्णयों को साझा करने, प्राप्त जानकारी को साफ़ और सुदृढ़ ढंग से प्रस्तुत करने का माध्यम प्रदान करता है।

    उदाहरण: विश्वविद्यालय की स्थिति रिपोर्ट:

    • डेटा विश्लेषण के परिणामों को एक आकर्षक और सुदृढ़ डैशबोर्ड के माध्यम से प्रस्तुत किया जा सकता है।
    • उदाहरण के लिए, आप विश्वविद्यालय के प्रवेश प्रक्रिया के परिणाम को ग्राफिकल चार्ट्स, ग्राफ्स और आवश्यक जानकारी सहित प्रस्तुत कर सकते हैं।
    • आपके डेटा विश्लेषण के परिणामों से प्राप्त की गई जानकारी को आप छात्रों और शिक्षकों के साथ साझा करके सुनिश्चित कर सकते हैं कि उन्हें आपके निर्णयों की समझ हो रही है।

    निष्कर्ष:

    परिणामों का प्रस्तुतीकरण डेटा एनालिटिक्स की सफलता की एक महत्वपूर्ण कुंजी होती है। जब आप अपने परिणामों को सरलता और सुदृढ़ता से प्रस्तुत करते हैं, तो आपकी जानकारी अधिक अच्छी तरीके से साझा होती है और आपके निर्णयों की मान्यता बढ़ती है।

    कदम 6: निर्णय और क्रियान्वयन Decision and Implementation

    जब हम डेटा एनालिटिक्स यात्रा के अंतिम चरण की ओर बढ़ते हैं, तो कदम 6 में जानकारियों को सूचित निर्णयों और व्यावसायिक क्रियान्वयन में परिणत करने की महत्वपूर्णता को महत्व दिया जाता है। इस लेख में, हम डेटा द्वारा प्रेरित निर्णय लेने की महत्वपूर्णता में और उन निर्णयों को प्रैक्टिकल रूप में क्रियान्वित करने में समझाएंगे।

    निर्णय और क्रियान्वयन की महत्वपूर्णता:The Essence of Decision and Implementation:


    पूरी डेटा एनालिटिक्स प्रक्रिया का उद्देश्य निर्णय लेने में मदद करना और वास्तविक दुनिया में क्रियान्वित करना होता है। कदम 6 में डेटा विश्लेषण से प्राप्त ज्ञान की दिशा में परिवर्तन किया जाता है जो कि वास्तविक निर्णयों में बदल सकते हैं जिनसे स्ट्रैटेजियों को आकार दिया जा सकता है, प्रक्रियाओं को अद्यतित किया जा सकता है, और संगठन के लक्ष्यों को प्राप्त किया जा सकता है।

    उदाहरण: खुदरा सामग्री का अनुकूलन: Retail Inventory Optimization:

    निर्णय: मान लीजिए कि आप एक खुदरा प्रबंधक हैं जो बिक्री डेटा का विश्लेषण कर रहे हैं ताकि सामग्री के स्तर को अनुकूलित किया जा सके। आपके डेटा विश्लेषण के माध्यम से, आपने सबसे ज्यादा बिकने वाले उत्पादों और उनके मौसमी पैटर्न की पहचान की है।
    क्रियान्वयन: आप जानकार निर्णय ले सकते हैं जैसे कि प्रमुख मौसमों से पहले लोकप्रिय आइटमों की संग्रह की स्तिथि को समायोजित करना, ताकि स्टॉकआउट और अधिशेष स्थितियों से बचा जा सके।


    उदाहरण: स्वास्थ्य संसाधन आवंटन:Healthcare Resource Allocation:

     

    निर्णय:कल्याण संस्थान के साथ काम कर रहे हैं और संसाधनों का प्रभावी आवंटन करने का योजना बना रहे हैं। डेटा विश्लेषण के माध्यम से, आपने विभिन्न चिकित्सा सेवाओं के लिए रोगी आवागमन के प्रवृत्तियों की पहचान की है।

    क्रियान्वयन: इन जानकारियों के साथ, स्वास्थ्य संसाधन कंप्लीट ज़र्स और सुविधाओं को अधिक प्रभावी ढंग से आवंटित कर सकते हैं ताकि

     

    Data Analytics Insights Predictive Power in Excel
    17Aug, 2023
    Data Analytics Insights Predictive Power in Excel



    Student Exam Scores

    StudentHours StudiedExam Score
    John275
    Emily385
    David160
    Sarah490
    Mark595
    Lisa270
    Alex698
    Emma382
    Michael488
    Olivia593

     

    Step 1: Data Entry

    Enter your data into an Excel spreadsheet. Place “Student” in A1, “Hours Studied” in B1, and “Exam Score” in C1. Then, input the corresponding data under each column.

    Step 2: Calculate Correlation

    • Select an empty cell (e.g., D1) for the correlation result
    • Enter the following formula:
    =CORREL(B2:B11, C2:C11)

    Press Enter to get the correlation value.

    This correlation value will help you understand the strength and direction of the relationship between hours studied and exam scores.

    The CORREL function calculates the Pearson correlation coefficient, which measures the strength and direction of the linear relationship between two sets of data. The result will be a number between -1 and 1:

    • A value of 1 indicates a perfect positive correlation, meaning that as one variable increases, the other also increases in a linear fashion.
    • A value of -1 indicates a perfect negative correlation, meaning that as one variable increases, the other decreases in a linear fashion.
    • A value of 0 indicates no linear correlation between the two variables.

     

    Step 3: Create Scatter Plot

    • Highlight the “Hours Studied” (B2:B11) and “Exam Score” (C2:C11) data.
    • Go to the “Insert” tab and choose “Scatter” from the charts group. Select a scatter plot option.
    • This will create a scatter plot that visually represents the relationship between the hours studied and exam scores.

    Step 4: Calculate Regression Analysis

    • Click on an empty cell (e.g., E1) where you want the regression analysis results.
    • Enter the following formula
    • =LINEST(C2:C11, B2:B11, TRUE, TRUE)
    • Press Ctrl+Shift+Enter as it’s an array formula.
    • The regression analysis will give you the intercept, slope, and other coefficients for the linear equation that best fits your data.

    Step 5: Interpret Regression Results

    • The results of the LINEST formula will provide an array of coefficients:
    • E1: Intercept (a)
    • E2: Slope (b)
    • E3: Standard Error of Intercept
    • E4: Standard Error of Slope
      These coefficients define the linear equation “Exam Score = Intercept + (Slope * Hours Studied)” that predicts exam scores based on hours studied
    • Slope: This number tells you how steep the line should be on the graph. It indicates whether, as one set of values (X) increases, the other set of values (Y) increases or decreases. A positive slope means they go up together; a negative slope means one goes up as the other goes down.
     
    • Intercept: This is the point where the line crosses the vertical Y-axis on the graph. It’s like the starting point for the line. When X is zero, this is where Y is on the graph.
     
    • Additional Statistics (if second “TRUE” is used):
     
    • Standard Error of the Regression: Think of this as a measure of how closely your data points cluster around the line. A smaller number means they’re closer to the line, indicating a better fit.
    • R-squared (R²) Value: This number shows how well the line fits your data. If it’s close to 1, it means the line does a good job of explaining the relationship between the two sets of data. If it’s closer to 0, the line doesn’t fit well.
    In essence, this information helps you understand the relationship between your two sets of data. The slope and intercept describe the line on the graph, and the additional statistics (if used) help you judge how well this line fits your data points. It’s valuable for making predictions and figuring out how strongly one set of data influences the other.
     
     
     
     

    After enabling the Data Analysis ToolPak, select “Regression” from the list and click “OK.”

    • The formula you’ve provided seems to be from Microsoft Excel or a similar spreadsheet software. The formula LINEST(C2:C11, B2:B11, TRUE, TRUE) is used to perform linear regression analysis on a set of data points. It calculates the parameters of the best-fit line that minimizes the squared differences between the observed data points and the values predicted by the line.

     

    Step 6: Create Regression Line on Scatter Plot

    • Right-click on one of the data points in the scatter plot.
    • From the context menu, choose “Add Trendline.”
    • In the Trendline Options, select “Linear” and check “Display Equation on chart.”
    • This will add a regression line to your scatter plot along with the equation of the line.
    • By following these steps, you’ve conducted correlation and regression analysis in Excel to understand the relationship between hours studied and exam scores. The correlation value tells you the strength of the relationship, and the regression analysis provides a predictive equation for exam scores based on hours studied. The scatter plot visually illustrates this relationship

    REGRESSION ANALYTIS THROUGH DATA ANALYTICS TOOLS

     This tool provides more comprehensive output than just the LINEST formula and includes statistics like coefficients, p-values, confidence intervals, and more.

    Enable Data Analysis ToolPak Excel

    • To enable the Data Analysis ToolPak in Excel, follow these steps:
    • Open Excel: Launch Microsoft Excel on your computer.
    • Navigate to Options: Click on the “File” tab in the top left corner to open the backstage view. Then, click on “Options” at the bottom of the left-hand menu. This will open the Excel Options dialog box.
    • Add-Ins: In the Excel Options dialog box, select “Add-Ins” from the left-hand menu.
    • Choose Excel Add-ins: In the Add-Ins window, at the bottom of the window, find and select “Excel Add-ins” from the dropdown menu next to “Manage” and click the “Go” button.
    • Select ToolPak: In the Add-Ins available list, locate and check the “Analysis ToolPak” checkbox. Optionally, you can also check “Analysis ToolPak – VBA” if you need to use it with VBA macros. Then click the “OK” button.
    • Install ToolPak: Excel will now install the Data Analysis ToolPak. If it prompts you to install or require any confirmation, follow the prompts.
    • ToolPak Tab: Once the installation is complete, you should see a new “Data” tab in Excel’s ribbon. The Data Analysis ToolPak options will be available under this tab.
    • Access Data Analysis ToolPak: To access the Data Analysis ToolPak, click on the “Data” tab, and you will find the “Data Analysis” button in the “Analysis” group. Click

    Load Data and Enable Data Analysis ToolPak

    • Open Excel.
    • Enter your data into columns, just like in your previous example. Let’s assume “Hours Studied” is in column B (B2:B11), and “Exam Score” is in column C (C2:C11).
    • Go to the “Data” tab.
    • Click on “Data Analysis” in the Analysis group.
    • If you don’t see “Data Analysis,” you might need to enable the Data Analysis ToolPak. Click on “Data Analysis,” and if it’s not listed, follow the prompts to enable it.

    Perform Regression Analysis

    data cleaning and charting in python with pandas matplotlib and seaborn
    7Aug, 2023
    Python Data Cleaning & Charting with Matplotlib and Seaborn

    Master Python Data Cleaning and Charting with Matplotlib and Seaborn.In t

    his hands-on tutorial, we’ll guide you through the essential data cleaning techniques in Python. Learn how to effectively handle missing data, apply various strategies to fill in the gaps, and optimize your datasets for analysis.

    Next, unleash the power of data visualization using Matplotlib and Seaborn. Discover how to create captivating charts, interactive plots, and stunning visualizations to convey complex insights with ease.

    Whether you’re a beginner or an experienced data scientist, this video will equip you with the skills to clean and visualize your data like a pro. Join us on this exciting journey of Python data cleaning and charting and take your data analysis skills to the next level!”

    Step 1: Import Pandas

    After installing Pandas, you need to import it into your Python script or Jupyter Notebook before using it. Importing it as “pd” is a common convention: 
    import pandas as pd

    Click to download data set

    data set for data cleaning in python

    Step 3: Read Data

    Pandas can read data from various sources like CSV files, Excel files, databases, etc. For this example, let’s assume you have a CSV file named “data.csv” with your data: 
    import pandas as pd


df = pd.read_excel(r'C:\Users\yogesh\Desktop\excel files for practice\messeydataset.xlsx')

# Display the DataFrame.
print(df)

    Step 3: Explore the Data 

    print(df.columns)
    In Python Pandas, the df.columns attribute is used to retrieve the column labels (column names) of a DataFrame. It returns a Pandas Index object containing the column names of the DataFrame. 
    Index(['Name', 'Age', 'Gender', 'City', 'Salary'], dtype='object')

    Step 4 :Fill the missing values in the 'Name

    Lorem ipsum dolor sit amet, consectetur adipiscing elit. Ut elit tellus, luctus nec ullamcorper mattis, pulvinar dapibus leo. 
    df['Name'].fillna('Unknown', inplace=True)
    The line of code df[‘Name’].fillna(‘Unknown’, inplace=True) is used to fill the missing values in the ‘Name’ column of the DataFrame df with the value ‘Unknown’. 
    print(df.Name)
    Output 
    0        John
1        Alex
2         Bob
3        Mary
4        Alex
5     Unknown
6         Tim
7     Unknown
8       Alice
9         Sam
10       navi
11      Sarah
12       roja
13       Mike
14     Olivia
15    Unknown
16       maya
17        Zoe
18    Unknown
Name: Name, dtype: object

    Step 5: Filling age missing value

    mean_age = df['Age'].mean()
df['Age'].fillna(mean_age, inplace=True) 
print(df.Age)

    The lines of code mean_age = df[‘Age’].mean() and df[‘Age’].fillna(mean_age, inplace=True) are used to calculate the mean of the ‘Age’ column and then fill the missing values in the ‘Age’ column with this mean value, directly modifying the original DataFrame df.

    OUTPUT below

    0     30.000000
1     25.000000
2     29.071429
3     32.000000
4     28.000000
5     29.071429
6     29.000000
7     31.000000
8     27.000000
9     29.071429
10    35.000000
11    33.000000
12    29.071429
13    26.000000
14    24.000000
15    28.000000
16    29.071429
17    30.000000
18    29.000000
Name: Age, dtype: float64

    Step 6: Fill most frequent Gender

    mode_gender = df['Gender'].mode().values[0]

# Fill missing 'Gender' values with the mode (most frequent) gender
df['Gender'].fillna(mode_gender, inplace=True)

# Print the DataFrame to see the updated values
print(df)

    Here’s a step-by-step explanation of the code:

    • mode_gender = df[‘Gender’].mode().values[0]: This line calculates the mode (most frequent value) of the ‘Gender’ column and stores it in the variable mode_gender.
    • df[‘Gender’].fillna(mode_gender, inplace=True): This line fills the missing values in the ‘Gender’ column with the value stored in the mode_gender variable. The fillna method is used to replace the missing values with the specified value. The inplace=True argument ensures that the changes are applied directly to the DataFrame df.
    • print(df): Finally, this line prints the updated DataFrame that now has the missing ‘Gender’ values filled with the mode (most frequent) gender.
    • After executing this code, you should see the DataFrame with the missing ‘Gender’ values replaced by the mode value, and all missing values in the ‘Gender’ column should be filled.

    Output

        0        Male
1      Female
2        Male
3      Female
4      Female
5      Female
6        Male
7      Female
8      Female
9      Female
10       Male
11     Female
12     Female
13       Male
14     Female
15    Seattle
16       Male
17     Female
18     Female
Name: Gender, dtype: object

     

    Step 7: Change 'Seattle' in the 'Gender' column with 'Female'

    It looks like there is a small error in your code. Assuming df is a pandas DataFrame and you want to replace the value ‘Seattle’ with ‘Female’ in the ‘Gender’ column, the correct code would be: 
    df['Gender'].replace({'Seattle': 'Female'}, inplace=True)
print(df.Gender)
    Output 
    0       Male
1     Female
2       Male
3     Female
4     Female
5     Female
6       Male
7     Female
8     Female
9     Female
10      Male
11    Female
12    Female
13      Male
14    Female
15    Female
16      Male
17    Female
18    Female
Name: Gender, dtype: object

    Step 8: Fill missing 'City' values with the mode (most frequent)

    mode_city = df['City'].mode().values[0]

# Fill missing 'City' values with the mode (most frequent) city
df['City'].fillna(mode_city, inplace=True)

# Print the DataFrame to see the updated values
print(df.City)
    In this code snippet, it appears that the DataFrame df contains a column named ‘City’, and there are some missing values in that column. The goal is to fill the missing ‘City’ values with the mode (most frequent) city in the DataFrame.

    Here’s a step-by-step explanation of what the code does:

    1. mode_city = df[‘City’].mode().values[0]: This line calculates the mode (most frequent) value of the ‘City’ column in the DataFrame df using the .mode() function. The result is stored in the variable mode_city.
    2. df[‘City’].fillna(mode_city, inplace=True): This line fills the missing values in the ‘City’ column of the DataFrame with the value stored in mode_city. The fillna() function is used for this purpose, and inplace=True means the changes are made directly to the DataFrame df without creating a copy.
    3. print(df.City): This line prints the ‘City’ column of the DataFrame df after filling the missing values with the mode city. The updated values will be displayed on the screen.
    As a result, the ‘City’ column will have no missing values, as they have been replaced with the mode city. OUTPUT 
    0           LA
1     San Fran
2           LA
3      Chicago
4      Houston
5      Chicago
6           LA
7      Houston
8       Boston
9        Miami
10       Miami
11     Houston
12    new york
13     Houston
14     Houston
15     Houston
16     Houston
17      Dallas
18      Dallas
Name: City, dtype: object
     

    Step 9: Fill missing 'Salary' values with the mean salary

    mean_salary = df['Salary'].mean()

# Fill missing 'Salary' values with the mean salary
df['Salary'].fillna(mean_salary, inplace=True)

# Print the DataFrame to see the updated values
print(df.Salary)
    Output 
    0     50000.0
1     60000.0
2     55000.0
3     50375.0
4     52000.0
5     45000.0
6     50375.0
7     48000.0
8     52000.0
9     42000.0
10    49000.0
11    51000.0
12    54000.0
13    47000.0
14    50375.0
15    52000.0
16    49000.0
17    51000.0
18    49000.0
Name: Salary, dtype: float64

    Step 10: Bar chart of Gender distribution:

    #Bar chart of Gender distribution:
import matplotlib.pyplot as plt
import seaborn as sns
sns.countplot(x='Gender', data=df)
plt.title('Gender Distribution')
plt.show()

    Here’s a step-by-step explanation of what the code does:

    1. import matplotlib.pyplot as plt: This line imports the pyplot module from the matplotlib library, which allows you to create visualizations like plots and charts.
    2. import seaborn as sns: This line imports the seaborn library, which is a powerful data visualization library built on top of matplotlib. It provides a high-level interface for drawing attractive and informative statistical graphics.
    3. sns.countplot(x=’Gender’, data=df): This line creates a count plot using seaborn’s countplot function. The x parameter specifies that the ‘Gender’ column should be represented on the x-axis, and the data parameter specifies the DataFrame from which the data will be taken.
    4. plt.title(‘Gender Distribution’): This line sets the title of the plot to ‘Gender Distribution’.
    5. plt.show(): This line displays the plot.

    Make sure that you have the correct data in the ‘Gender’ column of the DataFrame ‘df’ before running this code. If everything is set up correctly, this code will generate a bar chart showing the distribution of genders in your dataset.

    bar chart in distribution

    Step 11:Bar chart of Salary distribution:

    plt.hist(df['Salary'], bins=10, edgecolor='black')
plt.xlabel('Salary')
plt.ylabel('Count')
plt.title('Salary Distribution')
plt.show()

    Here’s a step-by-step explanation of the code:

    1. plt.hist(df[‘Salary’], bins=10, edgecolor=’black’): This line creates the histogram using the ‘Salary’ column from the DataFrame ‘df’. The ‘bins=10’ parameter specifies that the data will be divided into 10 equally spaced bins. The ‘edgecolor=’black” parameter sets the color of the edges of the bars in the histogram to black.
    2. plt.xlabel(‘Salary’): This line sets the label for the x-axis as ‘Salary’.
    3. plt.ylabel(‘Count’): This line sets the label for the y-axis as ‘Count’, representing the number of occurrences in each bin.
    4. plt.title(‘Salary Distribution’): This line sets the title of the plot to ‘Salary Distribution’.
    5. `plt.show:This line displays the histogram plot on the screen.

    Step 12:Bar chart of Age distribution:

    plt.hist(df['Age'], bins=10, edgecolor='black')
plt.xlabel('Age')
plt.ylabel('Count')
plt.title('Age Distribution')
plt.show()

    Here’s a breakdown of the code:

    1. plt.hist(df[‘Age’], bins=10, edgecolor=’black’): This line creates the histogram. It takes the ‘Age’ column from the DataFrame ‘df’ and plots it using the plt.hist function from the matplotlib library. The ‘bins’ parameter is set to 10, which means the histogram will have 10 bars, representing different age ranges. The ‘edgecolor’ parameter sets the color of the edges of the bars to black.
    2. plt.xlabel(‘Age’): This line sets the label for the x-axis to ‘Age’.
    3. plt.ylabel(‘Count’): This line sets the label for the y-axis to ‘Count’, representing the frequency of each age group.
    4. plt.title(‘Age Distribution’): This line sets the title of the histogram to ‘Age Distribution’.
    5. plt.show(): This line displays the histogram plot
    bar chart in python to show age distribution.

    Step 13:Pie chart of City distribution 

    city_counts = df['City'].value_counts()
plt.pie(city_counts, labels=city_counts.index, autopct='%1.1f%%', startangle=90)
plt.title('City Distribution')
plt.axis('equal')
plt.show()
    1. city_counts = df[‘City’].value_counts(): This line calculates the count of occurrences of each unique city in the ‘City’ column of the DataFrame df. The result is stored in the city_counts variable, which is a Pandas Series containing city names as index and their corresponding counts as values.
    2. plt.pie(city_counts, labels=city_counts.index, autopct=’%1.1f%%’, startangle=90): This line creates the pie chart. The plt.pie() function takes several parameters:
    3. city_counts: The data for the pie chart, which is the city_counts Series containing city counts.
    4. labels: The labels for each pie slice, which are the city names (obtained from city_counts.index).
    5. autopct=’%1.1f%%’: This parameter formats the percentage values displayed on each pie slice. ‘%1.1f%%’ formats the percentage with one decimal place.
    6. startangle=90: This parameter sets the starting angle for the first slice in degrees. In this case, it’s set to 90 degrees, which means the first slice will start from the 12 o’clock position.
    7. plt.title(‘City Distribution’): This line sets the title for the pie chart.
    8. plt.axis(‘equal’): This line ensures that the pie chart is displayed as a circle (i.e., an equal aspect ratio).
    9. plt.show(): This line displays the pie chart.

    FAQ

    Data cleaning, also known as data preprocessing, is the process of identifying and correcting errors or inconsistencies in datasets. It is crucial in data analysis because clean data ensures the accuracy and reliability of your analysis results.

    Common data cleaning tasks in Python include handling missing values, removing duplicates, converting data types, and dealing with outliers.

    You can handle missing values by using methods like dropna() to remove rows with missing values, fillna() to fill missing values with specific values, or interpolate() to fill missing values with interpolated values.

    Outliers can significantly affect statistical analysis and visualization. Removing or transforming outliers can help in producing more accurate and meaningful insights from the data.

     

    Some popular Python libraries for data cleaning are Pandas and NumPy. Pandas provides powerful tools for data manipulation, while NumPy is useful for numerical operations.

     

    Matplotlib and Seaborn are Python libraries used for data visualization. Matplotlib is a versatile library for creating various types of plots, while Seaborn is built on top of Matplotlib and provides a high-level interface for creating attractive statistical graphics.

     

    With Matplotlib, you can create line plots, bar plots, scatter plots, histograms, and more. Seaborn specializes in statistical visualization and offers functions for creating heatmaps, pair plots, and distribution plots.

     

    You can customize plots by modifying parameters such as color, style, labels, and titles. Matplotlib and Seaborn provide extensive customization options to tailor the visualizations to your specific needs.

     

    Unlocking the Hidden Mysteries of Data with Seaborn’s Visual Storytelling
    Example of machine learning for Data Analytics
    20Jul, 2023
    Top 10 Example of machine learning for Data Analytics

    Machine learning is important for data analytics because it enables businesses to gain insightful data and make well-informed choices.

    Machine learning is a branch of artificial intelligence (AI) that allows computer systems to learn and improve without being manually programmed. It lets businesses to fast, accurately, and efficiently process and analyze massive amounts of data, resulting in helpful findings and data-driven choices. Here are some of the most vital elements of machine learning:

    Types of Machine Learning:

    Supervised Learning


    In supervised learning, where the input and associated output are known, the system is trained using labeled data. In order to make predictions on brand-new, unknown data, the objective is to learn a mapping function from inputs to outputs.


    Unsupervised Learning


    Unsupervised learning is the process of training an algorithm to find patterns, structures, or correlations in unlabeled data without the use of explicit instructions.


    Reinforcement Learning:

    Reinforcement learning is the process of teaching agents to make decisions in a given environment in order to attain certain goals. The agent learns through trial and error after receiving feedback in the form of rewards or penalties.

    Key Concepts:

    Feature Extraction
    In order to make raw data suitable for machine learning algorithms, useful attributes (features) from the data must be selected and converted.
    Model Training
    In order to identify connections and patterns, the machine learning algorithm learns from the input data during model training. repeated iterations parameter adjustments are made to the model to reduce prediction errors.
    Model Evaluation:
    After training, the model’s performance is evaluated on a separate dataset to assess its ability to generalize to new, unseen data.
    Overfitting and Underfitting:
    Overfitting occurs when a model performs well on the training data but poorly on new data. Underfitting, on the other hand, happens when the model is too simple to capture the underlying patterns in the data.

    Machine learning is essential to data analytics because it enables companies and organizations to gain insightful information and make fact-based decisions. Ten uses of machine learning in data analytics are provided below:

    Predictive Analytics

    Predictive analytics is a subset of data analytics that predicts future outcomes or behaviors using historical data and machine learning algorithms. It involves measuring past data to find patterns and trends that can then be utilized to forecast future events. Predictive analytics is utilized extensively in a wide range of sectors and applications, including:

    Sales Forecasting

    Businesses can use predictive analytics to forecast sales quantities, recognize seasonal trends, and change their advertising campaigns accordingly.

    Customer Churn Prediction


    Companies may identify which customers are most likely to leave and put retention measures in place to lower the loss of clients by studying historical data and customer behavior.


    Financial Risk Assessment


    Predictive analytics is used by banks and other financial organizations to calculate credit risk, forecast loan defaults, and make data-driven loan choices.


    Healthcare Diagnostics and Prognostics


    In healthcare institutions, predictive analytics is used to assess patient risk factors, diagnose diseases, and forecast probable health effects.


    Inventory Management


    Predictive analytics can be used by businesses to improve inventory levels, lowering carrying costs while ensuring that supplies are available when required.


    Maintenance Optimization


    In order to determine when equipment is likely to fail, predictive maintenance employs sensor data and machine learning. This enables aggressive care to reduce expensive downtime.


    Marketing Campaign Optimization:


    Predictive analytics enables marketers to target the proper audience with customised content and offers, hence enhancing the efficacy of marketing campaigns.


    Demand Forecasting


    Predictive analytics may help producers and merchants in forecasting product demand and assuring sufficient stocks to fulfill client demands.

    Weather Prediction:

    Meteorologists use predictive analytics to forecast weather patterns and events, aiding in disaster preparedness and resource allocation.


    Energy Load Forecasting:

    Utilities use predictive analytics to forecast energy consumption, optimizing energy generation and distribution to meet demand efficiently.

    Recommendation Systems

    Recommendation systems, often known as recommender systems, are a type of information filtering system that predicts and offers suitable products or content to customers. These technologies are essential in a variety of online platforms and applications for customizing user experiences and increasing user engagement. There are various kinds of recommendation systems:

    Collaborative Filtering:
    Collaborative filtering recommends goods based on comparable users’ likes and actions. It recognizes individuals with similar preferences and recommends things that those users have liked or interacted with.
    Content-Based Filtering
    Content-based filtering suggests items to users based on the items’ features or attributes and the user’s preferences. It matches item features to user preferences to recommend related things.
    Hybrid Recommendation Systems
    Hybrid recommendation systems combine collaborative filtering and content-based filtering techniques to provide more accurate and diverse recommendations. By leveraging the strengths of both approaches, hybrid systems can overcome some limitations of individual methods.
    Matrix Factorization
    In collaborative filtering, matrix factorization is a method for reducing user-item interaction data into latent components. These variables indicate the fundamental qualities of the users and the products, which improves the recommendations.
    Context-Aware Recommendation
    Context-aware recommendation systems take additional contextual information into account, such as time, location, or user context, in order to deliver more relevant and timely recommendations.
    Knowledge-Based Recommendation
    Systems that use explicit knowledge about objects and user preferences to produce suggestions are called knowledge-based recommendation systems. When there is little information on user activities, this method is helpful.
    Session-Based Recommendation
    Recommendations are given in real time and with consideration for the user’s current session behavior by session-based recommendation systems.
    Association Rule Mining
    Association rule mining, which is extensively used in retail for cross-selling and upselling, identifies links and patterns between items frequently bought together.

     

    Natural Language Processing (NLP)

    NLP algorithms allow data analytics tools to extract insights from unstructured text data, such as customer reviews, social media posts, or survey responses.

    Here’s an example of how machine learning is used in Natural Language Processing (NLP) for data analytics:

    Sentiment Analysis:

    Sentiment analysis is a popular NLP application that involves using machine learning to determine the sentiment or emotion expressed in a piece of text. It’s commonly used to analyze customer feedback, social media posts, product reviews, and more.

    How it works:

    Data Collection:

    First, a large dataset of text data is collected, containing examples of positive, negative, and neutral sentiments.

    Data Preprocessing:

    The text data is cleaned and transformed into a format suitable for analysis. This includes removing special characters, converting text to lowercase, and tokenizing the sentences into individual words.

    Feature Extraction:

    NLP algorithms use various techniques to extract features or important information from the text. Common approaches include word embeddings, bag-of-words, or term frequency-inverse document frequency (TF-IDF) representations.

    Machine Learning Model:

    Once the data is preprocessed and the features are extracted, a machine learning model is trained on the labeled data. The model learns to recognize patterns in the text and associate them with specific sentiments.

    Sentiment Prediction:

    After the model is trained, it can predict the sentiment of new, unseen text. For example, given a customer review, the model can determine whether the review is positive, negative, or neutral.

    Use Cases:

    • E-commerce companies can use sentiment analysis to understand customer feedback and gauge customer satisfaction with their products or services.
    • Social media platforms can analyze user posts to identify trending topics and monitor public sentiment about certain events or products.
    • Market research firms can use sentiment analysis to analyze customer opinions and make data-driven decisions about product development and marketing strategies.

    In summary, sentiment analysis is an essential application of machine learning in NLP for data analytics, allowing businesses and organizations to gain valuable insights from textual data and make informed decisions based on customer sentiments.

     

    Anomaly Detection

    Machine learning can detect abnormal patterns in data, indicating potential fraud, errors, or unusual events, which is valuable for applications in finance, cybersecurity, and healthcare.

    Anomaly detection is a type of machine learning technique used to identify unusual patterns or outliers in a dataset. It is widely used in various domains such as fraud detection, network intrusion detection, fault detection, and more. The goal is to distinguish normal behavior from abnormal behavior, which can be critical in detecting potential issues or threats.

    Clustering

    The most common clustering algorithms include K-Means, Hierarchical Clustering, and DBSCAN (Density-Based Spatial Clustering of Applications with Noise). Let’s briefly explain each of them:

    K-Means Clustering:


    K-Means is one of the simplest and most widely used clustering algorithms. It aims to partition data into K clusters, where K is a user-defined parameter. The algorithm works as follows:

    1. Randomly initialize K cluster centroids
    2. Assign each data point to the nearest centroid.
    3. Recalculate the centroids as the mean of all data points assigned to that cluster.
    4. Repeat the assignment and centroid update steps until convergence or a maximum number of iterations is reached.

    Hierarchical Clustering:


    Hierarchical Clustering creates a tree-like hierarchical representation of data points. It can be agglomerative (bottom-up) or divisive (top-down). The algorithm works as follows:

    • Initially, each data point is treated as a separate cluster.
    • Merge or split clusters based on a similarity metric until all data points belong to a single cluster or meet a predefined stopping criterion.


    DBSCAN (Density-Based Spatial Clustering of Applications with Noise):


    DBSCAN is a density-based clustering algorithm that groups data points based on their density. It defines clusters as regions of high-density separated by regions of low-density. The algorithm works as follows:

    • Given two parameters: eps (neighborhood radius) and min_samples (minimum number of points to form a cluster).
    • A data point is considered a core point if it has at least min_samples points within its neighborhood of radius eps.
    • Form a cluster by connecting core points within each other’s neighborhood. Any point not reachable by any core point is considered an outlier.
      Example of K-Means Clustering:

    Let’s consider a 2-dimensional dataset for simplicity. Assume we have the following data points:

    [(2, 3), (3, 3), (2, 4), (5, 6), (6, 6), (5, 5)]
    Using K-Means with K=2, the algorithm might group the data points into two clusters:

    Cluster 1: [(2, 3), (3, 3), (2, 4)]
    Cluster 2: [(5, 6), (6, 6), (5, 5)]

    The algorithm would find centroids for each cluster, e.g., centroid_1 = (2.33, 3.33) and centroid_2 = (5.33, 5.67).

    Please note that the quality of clustering results can vary depending on the choice of the algorithm, the number of clusters (K), and the nature of the data. It is often essential to preprocess the data and choose appropriate evaluation metrics to assess the performance of clustering algorithms.

     

     

    Regression Analysis

     Regression models help data analysts understand relationships between variables, making predictions and identifying correlations within datasets.

    Regression analysis is a fundamental statistical technique used in machine learning and data analysis to model the relationship between a dependent variable (also known as the target or outcome variable) and one or more independent variables (also known as predictor or feature variables). The goal of regression analysis is to create a mathematical model that can predict the dependent variable based on the values of the independent variables.

    In the context of machine learning, regression is commonly used for tasks where the output is a continuous value. Some examples of regression problems include predicting housing prices, stock prices, temperature, sales figures, etc.

    Step by Step Guide to Know Machine Learning for Data Analytics
    9 ways to increase your digital presence for Data-Analytics Job
    17Jul, 2023
    9 ways to increase your digital presence for Data-Analytics Job
       

    To have a solid online presence, it’s important to be consistent, provide high-quality material, and engage your audience. Keep your profiles up to date, provide useful details, and network with others in the data analytics industry to increase your visibility and draw in potential employers.

    Showcase Data Analytics Projects

    Make a portfolio that features your data analytics work. Draw attention to the business issues you resolved, the techniques you employed, and the outcomes you obtained. On your website, LinkedIn, and other channels that are appropriate, share this portfolio.You can convince potential employers or clients of your skills and abilities by showcasing your data analytics efforts. 

    Example:

    1.Project Overview:


    Give a brief explanation of the project’s goals. For instance, this project’s objective was to group customers of an online retailer according to their behavior and preferences in order to inform their marketing efforts.


    2. Business Problem


    Specify what business issue the initiative was designed to address. For instance, by customizing marketing campaigns to various consumer segments, the e-commerce company hoped to improve customer targeting and increase customer engagement.


    3. Data Collection and Preparation


    Describe the procedure for gathering and preparing data. Explain the sources of the data used, the steps that were followed to clean and change the data, and any challenges that were encountered.


    4. Exploratory Data Analysis (EDA):


    The results of the exploratory data analysis should be presented. To showcase information about customer demographics, purchasing trends, or engagement metrics, use visualizations like charts, graphs, or interactive dashboards.


    5. Customer Segmentation Methodology:


    Describe the technique utilized for client segmentation. This could incorporate segmentation features like purchase frequency, overall spending, or product preferences, as well as clustering algorithms like K-means or hierarchical clustering.


    6. Segmentation Results


    Present the segmentation results in a clear and visually appealing format. Show the identified customer segments, their characteristics, and any unique traits or patterns observed within each segment.


    7. Business Impact and Recommendations:


    Highlight how consumer segmentation affects the marketing activities of the e-commerce organization. Any advancements in consumer targeting, engagement, or revenue generating should be discussed. On the basis of the segment information, make specific recommendations for customized marketing efforts.


    8. Conclusion:

    Summarize the project outcomes and emphasize the value generated through customer segmentation. Discuss the potential for further analysis or future enhancements.

     

    Build a Professional Website

    Make a personal website or portfolio to display your projects, abilities, and knowledge in data analytics. By including useful material and relevant keywords, you may improve your website’s search engine optimization.

    1.Define Your Goals and Audience: 

    Determine the purpose of your website and identify your target audience. Understand what you want to achieve through your website, whether it’s attracting potential employers, clients, or networking with professionals in your field.

    2. Choose a Domain

    Name: Select a domain name that reflects your personal brand or professional identity. It should be memorable, relevant to your field, and easy to spell. Use domain registrar platforms like GoDaddy or Namecheap to register your domain.

    Select a Web Hosting Provider: Choose a reliable web hosting provider that offers suitable hosting plans for your needs. Consider factors like server uptime, security features, bandwidth, and customer support. Popular hosting providers include Bluehost, SiteGround, and HostGator.

    3. Plan Your Website Structure and Content:

    Outline the structure of your website, including main pages, sub-pages, and navigation. Determine the content you want to include, such as an about me section, portfolio, blog, contact information, and any additional sections specific to your field.

    4. Choose a Content Management System (CMS):

    Select a CMS to build and manage your website. Popular CMS options are WordPress, Wix, and Squarespace. They provide user-friendly interfaces and a wide range of customizable templates and themes.

    5. Design and Customize Your Website:

    Choose a professional and visually appealing website template or theme that aligns with your personal brand and industry. Customize the colors, fonts, and layout to create a cohesive and visually appealing website.

    6. Create Compelling Content:

    Craft high-quality content for each page of your website. Clearly communicate your skills, experience, and achievements. Showcase your data analytics projects, certifications, and any notable accomplishments. Use engaging language and highlight your unique selling points.

    7. Optimize Your Website for Search Engines:

    Implement search engine optimization (SEO) techniques to improve your website’s visibility in search engine results. Research relevant keywords and incorporate them naturally in your content, page titles, headings, and meta descriptions.

    Include a Portfolio or Project Showcase: Create a dedicated section to showcase your data analytics projects, case studies, or notable achievements. Include project descriptions, methodologies used, outcomes, and any visualizations or reports you created.

    Provide Contact Information:

    Make it easy for visitors to get in touch with you. Include a contact page with your email address, social media profiles, and any other preferred means of communication. Consider adding a contact form to streamline inquiries.

    Mobile Responsiveness:

    Ensure your website is mobile-responsive, meaning it displays well on different devices and screen sizes. Test your website across various devices and browsers to ensure a consistent and user-friendly experience.

    Regularly Update and Maintain Your

    Website: Keep your website up to date by regularly adding new content, updating your portfolio, and blogging about relevant topics. Regularly check for broken links, optimize page loading speed, and ensure the website’s overall functionality.

    Remember to keep your website professional, visually appealing, and easy to navigate. Regularly update and maintain your website to reflect your latest achievements and skills. Your website is a reflection of your personal brand, so invest time and effort to create a compelling online presence.

    Enhance Your LinkedIn Profile

    Professional Headline
    Create an appealing headline that highlights your knowledge, value proposition, or current position. To grab the interest of potential contacts and employers, it should be concise and engaging.
    Profile Photo:
    Select a photograph for your profile that is of good quality and looks professional. Make sure your face is seen and that you come across as approachable and amiable.
    Background Image
    Make your backdrop image unique to express your personal or business brand. You can use a pertinent photo, such as a representation of your sector or one with a setting consistent with your personal brand.
    Summary
    Create a compelling summary which highlights your qualifications, work history, and professional aspirations. Focus on showing your distinct value proposition while using clear language. To structure the material and make it easier to read, use bullet points or paragraphs.
    Experience
    Create an engaging summary that emphasizes your qualifications, work history, and professional aspirations. Focus on showing your distinct value proposition while using clear language. To structure the material and make it easier to read, use bullet points or paragraphs.
    Experience
    Create an engaging summary that emphasizes your qualifications, work history, and professional aspirations. Focus on showing your distinct value proposition while using clear language. To structure the material and make it easier to read, use bullet points or paragraphs.
    Skills and Endorsements
    Enhance your profile with pertinent abilities so that you may be found in search results and show off your areas of experience. Make connections with coworkers and friends who can attest to your abilities since attestations give your profile more credibility.
    Education and Certifications
    Include all relevant information about your education, such as degrees, certificates, and courses. If you have any honors, awards, or educational endeavors that are relevant, mention them.

    Create and Share Valuable Content

    Make a name for yourself as a thought leader in data analytics by producing excellent content. Create a blog, submit guest posts to trade publications, or share knowledge on social media. This promotes your subject-matter expertise and draws in the right kind of audience.

    Define your audience: Identify who you want to reach and tailor your content to their interests and needs.

    Educational content: Share industry insights, tips, or how-to guides that provide value to your audience.

    Thought leadership: Share unique perspectives on industry trends, challenges, or innovations to establish yourself as an authority.

     

    Visual appeal: Use attention-grabbing visuals like images or charts to make your content more engaging and shareable.

    Engage with others: Interact with your network by liking, commenting, and sharing their content to build relationships.

    Consistency: Develop a content schedule to regularly share valuable content and stay top of mind.

     

    Hashtags and keywords: Use relevant hashtags and keywords to increase visibility and attract a broader audience.

    Measure and adapt: Monitor engagement metrics and adjust your content strategy based on what resonates with your audience.Cross-promote: Share your LinkedIn content on other platforms to reach a wider audience and invite engagement.

    Utilize Social Media Sites

    Make use of social media sites like Twitter, Facebook, and Instagram to share content, interact with others in the industry, and take part in pertinent discussions. Join communities and groups focused to data analytics to expand your visibility and network.

    Choose platforms: Select relevant social media platforms based on your target audience and industry.

    Engage and share: Actively engage with others, share valuable content, and participate in discussions.

    Consistency: Maintain a regular presence by posting consistently and interacting with your audience.

    Visual appeal: Use compelling visuals to grab attention and make your posts more engaging.

    Analytics: Monitor analytics to understand what content resonates with your audience and adjust accordingly.

    By leveraging social media sites effectively, you can build your online presence, connect with industry professionals, and amplify your message.

    Participate in Online Forums and Communities

    Join online communities and forums dedicated to data analytics, including Reddit, Quora, or Stack Exchange. Answer queries, participate in discussions, and offer insightful commentary. This increases your exposure and builds your credibility.

    Contribute to Open-Source Projects:

    Participate in open-source data analytics projects on GitHub and other websites. This exhibits your expertise, capacity for teamwork, and dedication to the subject. You can network with future employers as well as other contributors.

    Find projects: Discover open-source projects aligned with your interests and skills.

    Contribute: Contribute code, documentation, bug fixes, or feature enhancements to the project.

    Collaborate: Engage with the project’s community, participate in discussions, and offer assistance to fellow contributors.

    Build your skills: Gain practical experience, learn from experienced developers, and enhance your coding abilities.

    Showcase your work: Highlight your contributions on platforms like GitHub to demonstrate your expertise to potential employers.

    Contributing to open-source projects can expand your network, improve your coding skills, and showcase your abilities to the wider developer community

    Participate in and Speak at Sector Events:

    Attend conferences, seminars, webinars, and meetups focusing on data analytics. Establish connections with business leaders, present at events to share your expertise, and take an active part in panel discussions. By doing this, you become more well-known and are recognized as an authority.

    Network: Connect with industry professionals, colleagues, and mentors to build a supportive network.

     

    Seek recommendations: Request recommendations from trusted individuals who can vouch for your skills and work ethic.

     

    Professional groups: Join relevant professional groups or communities to access support, guidance, and resources.

     

    Online forums: Participate in online forums or discussion boards where you can ask questions and seek advice from experts.

     

     

    By leveraging support and recommendations from your network and online communities, you can gain valuable insights, expand your opportunities, and establish credibility in your field.

     

    Find Support and Recommendations

    Ask for recommendations and endorsements from colleagues, clients, or mentors who can attest to your data analytics expertise. These endorsements give your online presence more authority and raise the likelihood that employers will take notice of you.

    Stay Updated and Contribute Insights:

    Keep up with the most recent data analytics trends, methods, and technologies. Through blog entries, social media updates, or video content, express your thoughts and ideas on relevant topics. This establishes you as an experienced professional and fosters relationships with others in the field.

    Stay informed: Continuously update your knowledge by following industry news, trends, and advancements.


    Engage in discussions: Share your insights, opinions, and expertise by actively participating in relevant industry discussions and forums.


    Share valuable content: Contribute thought-provoking articles, blog posts, or social media updates that provide value to your network.

    Attend events: Participate in conferences, webinars, or workshops to stay updated and connect with professionals in your field.

    By staying updated and actively contributing your insights, you can establish yourself as a knowledgeable professional, expand your network, and foster meaningful discussions within your industry.


    Top points for Entry level data analyst resume for freshers in 2023

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