Certainly! Below is a documentation template for the complex Predictive Analytics for Sales project:

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Predictive Analytics for Sales Documentation

Overview

Predictive Analytics for Sales is a complex Python project aimed at forecasting sales figures based on historical data. The project incorporates advanced techniques such as feature engineering, ensemble learning, hyperparameter tuning, and model interpretation to build a robust predictive model.

Features

• Preprocessing of data including handling missing values, scaling numerical features, and encoding categorical features.
• Utilization of various regression models such as RandomForestRegressor, GradientBoostingRegressor, XGBRegressor, and LGBMRegressor.
• Implementation of ensemble learning techniques including StackingRegressor with a meta-estimator.
• Hyperparameter tuning using GridSearchCV to optimize model performance.
• Feature selection to identify the most important features for predicting sales.
• Visualization of predicted vs. actual sales values and feature importance.

Installation

1. Clone the repository:

   git clone https://github.com/your_username/predictive-analytics-for-sales.git
   

2. Navigate to the project directory:

   cd predictive-analytics-for-sales
   

3. Install the required dependencies:

   pip install -r requirements.txt
   

Usage

1. Prepare your sales data in CSV format with columns including 'Date', 'Sales', 'Marketing Channel', 'Marketing Spend', 'Promotions', 'Competitor Price', and 'Weather'.
2. Modify the sales_data.csv file or replace it with your own dataset.
3. Run the main Python script:

   python main.py
   

4. The script will preprocess the data, train the predictive model, perform hyperparameter tuning, and evaluate the model's performance.
5. The results including Mean Squared Error, R-squared, and visualizations will be displayed in the console and saved in the project directory.

Configuration

• Adjust hyperparameters and model configurations in the main.py script.
• Modify feature selection parameters and model interpretation techniques as needed.
• Customize visualization settings to suit your preferences.

Dependencies

• pandas
• numpy
• matplotlib
• seaborn
• scikit-learn
• xgboost
• lightgbm