Revelation-coder/Retail-Sales-Forecasting

this repository is for the development of a random forest regressor powered machine learning model to forecast grocery sales of ten (10) products for small scale retailers in Zimbabwe. #Sales Prediction App 1. This Streamlit app allows you to predict sales for Small scale retail business owners to predict sales for Different products based on a trained Random Forest model. 2. Requirements Python 3.6 or higher Streamlit scikit-learn pandas pickle Install the required packages using pip: pip install streamlit scikit-learn pandas pickle 3. Setup Train and save the model: You need to have already trained a Random Forest Regression model and saved it as rf_model.pkl. Make sure this file is in the same directory as app.py. Prepare the sales data: Make sure your sales data is in a CSV file named train.csv. The CSV should contain columns for date, store, item, and sales (adjust column names if needed). Run the app: Open a terminal in the directory where you saved the files and run the following command: streamlit run app.py 4. Usage The app will open in your browser. Enter the start and end dates for the prediction period. Select the product you want to predict sales for. Click the "Predict Sales" button. The app will display a table showing the predicted sales for Each day in the selected period, along with the total predicted sales. 5.Notes The model is trained on the train.csv data. The item column in the CSV should contain numerical IDs representing different products. The product_names dictionary in the code maps these IDs to actual product names. You might need to modify this dictionary based on your data. The code assumes that you want to predict sales for store 1. You can change this in the prediction_data DataFrame. 1. Sales Prediction App for Small-Scale Retailers in Zimbabwe This repository contains the code for a Streamlit app that predicts sales for small-scale retailers in Zimbabwe using a Random Forest machine learning model. The app is based on the research project documented in the provided project documentation. 2. Project Background The research project aims to improve demand f …