📈 Sales Prediction Project

This project is part of the CodeAlpha Internship Program and focuses on building a Sales Prediction model using supervised machine learning techniques. It involves data preprocessing, exploratory data analysis (EDA), model training, and performance evaluation to forecast future sales.

🚀 Project Overview

The goal of this project is to predict sales based on historical data using regression models. The notebook walks through the steps of:

• Loading and preprocessing the dataset
• Exploring relationships in the data
• Training a linear regression model
• Evaluating the model using appropriate metrics

📂 Files

• CodeAlpha_Sales_Prediction.ipynb: The main Jupyter Notebook containing all code and analysis.
• data/: (Optional) Folder for storing dataset files if added later.

🧰 Tools & Libraries Used

• Python 3.x
• Jupyter Notebook
• pandas
• numpy
• matplotlib
• seaborn
• scikit-learn

📊 Features

• Data Cleaning & Preprocessing: Handles missing values and formats the data for modeling.
• Visualization: Utilizes Seaborn and Matplotlib to understand feature relationships.
• Model Building: Implements Linear Regression for sales forecasting.
• Performance Evaluation: Assesses model accuracy using metrics like R² and Mean Squared Error (MSE).

🧠 How It Works

1. Load Data: Reads the dataset into a DataFrame.
2. EDA: Visualizes feature distributions and correlations.
3. Preprocess: Converts categorical variables and scales numerical features.
4. Train Model: Fits a Linear Regression model.
5. Evaluate Model: Prints R² score and plots prediction vs. actual sales.

🏁 Getting Started

To run this project locally:

1. Clone the repository:

   git clone https://github.com/your-username/sales-prediction.git
   cd sales-prediction