The Sales Prediction Project focuses on forecasting future sales using historical data and data analytics techniques. The objective of this project is to help businesses make data-driven decisions by identifying trends, patterns, and key factors influencing sales performance. In this project, I worked on collecting and preprocessing sales data, including handling missing values, removing inconsistencies, and preparing the dataset for analysis. I performed exploratory data analysis (EDA) to understand sales patterns, seasonal trends, and customer behavior. I used machine learning models such as Linear Regression (or mention your model: Decision Tree / Random Forest if used) to build a predictive model that estimates future sales based on features like product category, time, and past sales data. The model was evaluated using performance metrics such as Mean Absolute Error (MAE) and R-squared score to ensure accuracy. Additionally, I visualized the results using charts and graphs to clearly communicate insights. This project helped improve my skills in data analysis, model building, and using tools like Python, Pandas, Matplotlib, and Scikit-learn.