**Prediction with Multiple Linear Regression **Overview This repository contains two comprehensive projects that demonstrate the application of multiple linear regression for predictive modeling. The projects focus on predicting profit and car prices based on various features and attributes, using datasets from different domains.

**Projects Included **50_Startups Profit Prediction Toyota Corolla Price Prediction **Project 1: 50_Startups Profit Prediction **Objective The primary goal of this project is to build a multiple linear regression model to predict the profit of a startup based on key business expenditures such as R&D Spend, Administration, Marketing Spend, and State.

#Solution **#Exploratory Data Analysis (EDA): Conducted in-depth analysis to understand the relationships between variables and identify key predictors. ****#Modelling: Developed a multiple linear regression model using the features identified in the EDA. ****#Evaluation: The model’s performance was evaluated using the R² score, ensuring its reliability in predicting profits. **#Business Impact This model aids startups in making data-driven decisions regarding budget allocation across different departments, ultimately helping to maximize profits.

**##Project 2: Toyota Corolla Price Prediction **#Objective This project aims to predict the selling price of a Toyota Corolla based on various features such as age, mileage, horsepower, engine size, and other relevant car attributes.

**Solution **Exploratory Data Analysis (EDA): Visualized the relationships between car features and their impact on price to identify significant predictors. #Modelling: Built a multiple linear regression model that accurately predicts car prices using the selected features. #Evaluation: The model was assessed using the R² score, ensuring it provides accurate pricing predictions. #Business Impact This model supports car dealerships and sellers in setting competitive and accurate prices for used cars, enhancing customer satisfaction and optimizing inventory management.

**#Repository Structure ****#datasets/: Contains the datasets used for both projects. ****#notebooks/: Includes Jupyter notebooks with the full code for data analysis, modeling, and evaluation. ****#presentations/: PowerPoint presentations summarizing the objective, solution, and business impact of each project. ****#docs/: A Word document detailing the entire process, including solution architecture, methodology, challenges, and more. ****#graphs/: Contains all the visualizations generated during the EDA process. ****Getting Started **Prerequisites Python 3.x Jupyter Notebook Required libraries: pandas, numpy, seaborn, matplotlib, scikit-learn Installation Clone the repository to your local machine:

bash Copy code git clone https://github.com/yourusername/Prediction-with-Multiple-Regression.git Navigate to the project directory and install the required dependencies:

bash Copy code pip install -r requirements.txt Usage #50_Startups Project: Open the notebooks/50_Startups_Analysis.ipynb notebook to explore the data, build the model, and evaluate its performance. Toyota Corolla Project: Open the notebooks/Toyota_Corolla_Analysis.ipynb notebook for a detailed walkthrough of the car price prediction process. Results #50_Startups Model: Achieved an R² score of X, indicating the model's effectiveness in predicting profit. #Toyota Corolla Model: Achieved an R² score of X, demonstrating the model's accuracy in predicting car prices. Challenges and Learnings #50_Startups: Handling categorical variables and ensuring the model's interpretability were key challenges. Toyota Corolla: Selecting the most impactful features from a large dataset required careful analysis.

#Keywords Multiple Linear Regression Predictive Modeling Data Science Machine Learning Python Business Analytics Car Price Prediction Profit Prediction