This project performs an analysis on a sales dataset and builds a predictive model for sales forecasting using Linear Regression. It involves several steps like Exploratory Data Analysis (EDA), data visualization, and model training with the goal of predicting future sales.
Project Overview Goal: To analyze sales data and build a model to forecast future sales. Technologies Used: Python Pandas, NumPy Matplotlib, Seaborn Scikit-learn (Linear Regression, GridSearchCV)
Features Exploratory Data Analysis (EDA): Analyzing the sales data to uncover trends, seasonality, and customer behavior. Data Visualization: Creating insightful visualizations like bar charts, heatmaps, and trend lines to highlight key business insights. Sales Forecasting: Building a predictive model using Linear Regression to forecast future sales. Hyperparameter Tuning: Used GridSearchCV to find the optimal hyperparameters for the model.
Dataset - sales_data_sample.csv file The dataset used in this project includes historical sales data, including variables like: Sales (dependent variable) Date, customer information, product categories, etc.
Contributing: Feel free to fork the repository, submit issues, or open pull requests if you'd like to contribute to this project.