https://github.com/shyamkumarnagilla/big-sales-prediction
The "Big Sales Prediction" model is a machine learning project that aims to accurately forecast sales for a given period. The model utilizes the Random Forest Regressor algorithm, a powerful ensemble learning technique, to analyze historical sales data and make predictions. It can be valuable for businesses looking to optimize sales forecasting.
https://github.com/shyamkumarnagilla/big-sales-prediction
data-analytics data-preprocessing data-science data-visualization machine-learning model-evaluation model-training
Last synced: 4 months ago
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The "Big Sales Prediction" model is a machine learning project that aims to accurately forecast sales for a given period. The model utilizes the Random Forest Regressor algorithm, a powerful ensemble learning technique, to analyze historical sales data and make predictions. It can be valuable for businesses looking to optimize sales forecasting.
- Host: GitHub
- URL: https://github.com/shyamkumarnagilla/big-sales-prediction
- Owner: Shyamkumarnagilla
- Created: 2024-07-10T09:05:03.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2024-10-22T13:24:55.000Z (about 1 year ago)
- Last Synced: 2025-01-24T17:15:57.926Z (10 months ago)
- Topics: data-analytics, data-preprocessing, data-science, data-visualization, machine-learning, model-evaluation, model-training
- Language: Jupyter Notebook
- Homepage:
- Size: 2 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
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Metadata Files:
- Readme: README.md
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README
# Big Sales Prediction
This project focuses on predicting big sales using the Random Forest Regressor model.
The aim is to build an accurate and robust machine learning model that can forecast sales based on historical data and various features such as store size, location, and promotional activities.
**Features**
Data Preprocessing: Handling missing values, encoding categorical variables, and scaling features.
Model Training: Implementing the Random Forest Regressor to train on the processed data.
Model Evaluation: Using metrics such as Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE) to evaluate model performance.
Visualization: Plotting feature importances and sales predictions to understand model behavior.
**Results**
The model predicts sales with a high degree of accuracy, helping businesses make informed decisions about inventory management, marketing strategies, and resource allocation.
**Dataset used:** https://github.com/YBI-Foundation/Dataset/raw/main/Big%20Sales%20Data.csv