In this assignment, you will work with a retail sales dataset$($provided in csv file format$)$ to perform not only data analysis but also predictive modeling. You will explore the data, handle missing values, and build a machine learning model to predict key outcomes. Finally, you will save the trained model and integrate it into a web or mobile application. $($Use Anaconda and Jupyter $)$

Dataset Columns:

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invoice$\_$id: Unique identifier for each transaction.

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branch: The branch where the transaction took place.

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city: The city in which the branch is located.

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customer$\_$type: Type of customer $($e$.$g$.,$ Member, Non$-$member$).$

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gender$\_$customer: Gender of the customer.

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product$\_$line: Category of the product purchased.

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unit$\_$cost: Cost per unit of the product.

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quantity: Number of units purchased in the transaction.

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$5$pct$\_$markup: The $5\%$ markup applied to the unit cost.

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revenue: Total revenue generated from the transaction.

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date: The date of the transaction.

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time: The time the transaction occurred.

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payment$\_$method: The payment method used by the customer.

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cogs: Cost of Goods Sold $($COGS$)$ for the transaction.

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gm$\_$pct: Gross Margin Percentage.

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gross$\_$income: Gross income from the transaction.

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rating: Customer satisfaction rating for the transaction.

Tasks:

$1.$

Data Cleaning and Preparation:

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Imputation: Identify and impute missing values using appropriate techniques $($e$.$g$.,$ mean$/$median for numerical data, mode for categorical data, or predictive imputation methods$).$

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Convert data types if necessary $($e$.$g$.,$ ensure date and time columns are in the correct format$).$

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Feature engineering: Create any additional features that could enhance the predictive power of your model, such as a "time of day" category derived from the time column.

$2.$

Descriptive Statistics and Data Exploration:

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Calculate summary statistics for numerical columns and explore the distribution of categorical columns.

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Create visualizations to explore patterns in the data, such as the distribution of revenue and gross income across branches and product lines.

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Explore relationships between key variables, such as the impact of quantity and unit cost on revenue and gross income.

$3.$

Predictive Modeling:

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Model Selection: Choose at least four machine learning models to predict either revenue, gross$\_$income or rating.

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Training and Testing: Split the dataset into training and testing sets. Train the model using the training data and evaluate its performance on the testing data.

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Hyperparameter Tuning: Optimize your model's performance by tuning hyperparameters using cross$-$validation.

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Categorization: Use the trained model to categorize transactions into different performance segments $($e$.$g$.,$ high revenue vs$.$ low revenue transactions$).$

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Model Evaluation: Evaluate the model using appropriate metrics $($RMSE accuracy, precision or F$1).$

$4.$

Saving and Deploying the Model:

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Model Saving: Save the trained model to a file using a format like joblib or pickle.

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API Development: Develop a REST API using Flask or FastAPI to serve the model, allowing it to be accessed by the application.

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Frontend Integration: Incorporate the model's predictions into a web or mobile interface. This could involve displaying predictive insights to users or categorizing transactions in real$-$time.

Deliverables:

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A detailed report covering data cleaning, analysis, and predictive modeling, including visualizations and statistical insights

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A Notebook with your code, analysis and comments

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The saved model file $($e$.$g$.,.$joblib or $.$pkl$).$

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Integration with a web or mobile interface.