📈 Profit Prediction Analysis

Exploratory Data Analysis & Machine Learning (Regression)

📌 Project Overview

This project leverages Python and its powerful data science libraries to build a predictive model for business profit values. By employing Exploratory Data Analysis (EDA) and Regression techniques, the goal is to provide a dependable tool that facilitates informed, data-driven decision-making for stakeholders.

🚀 How to Run

1. Clone the repository: git clone https://github.com/suryateja015/Exploratory-data-analytsis-for-predicting-company-profits.git
2. Install dependencies: pip install pandas numpy matplotlib seaborn scikit-learn
3. Open DATA SCIENCE PROJECT.ipynb in Jupyter Notebook or VS Code.

📊 Key EDA Insights

• Feature Correlation: Identified the strongest predictors of profit (e.g., R&D Spend vs. Marketing).
• Data Integrity: Handled missing values and removed outliers to ensure model reliability.
• Visualizations: Includes Heatmaps, Scatter Plots, and Distribution curves to understand data patterns.

🛠️ Tech Stack

• Language: Python 3.x
• Libraries:
  • Pandas & NumPy (Data Manipulation)
  • Matplotlib & Seaborn (EDA Visualizations)
  • Scikit-Learn (Regression Modeling & Evaluation)

📈 Results

The final regression model achieved an R² Score of [Insert Your Score, e.g., 0.94], indicating high accuracy in predicting profit based on the provided business metrics.