📊 Data Analysis with Python Tools

Welcome to my comprehensive Data Analysis project using Python! This repository covers the full data analysis pipeline — from data importing and cleaning, through exploratory analysis and visualization, to model development and evaluation. It includes practical applications on real-world datasets like car prices, laptop specifications, house sales, and medical insurance.

📁 Repository Structure

🗃️ Datasets

This repository contains the following datasets in .csv format:

1. auto.csv, automobile.csv, module_5_auto.csv: Automotive datasets for price prediction and regression modeling.

2. kc_house_data_NaN.csv: Real estate data for predicting house prices.

3. medical_insurance_dataset.csv: Data for predicting insurance charges.

4. laptop_pricing_dataset_mod2.csv, Laptops.csv: Laptop pricing and specification datasets for regression and classification.

5. clean_df.csv: Pre-cleaned dataset used in analysis workflows.

📓 Notebooks

• 📥 Data Importing & Cleaning 1. Importing_and_Understanding_data.ipynb 2. Data-Wrangling.ipynb 3. practice_data_wrangling_LaptopData.ipynb

• 📊 Exploratory Data Analysis (EDA) 1. Exploratory_data_analysis_cars.ipynb

    2. *Practice_Exploratory_data_analysis_Laptop.ipynb*
  

• 🧠 Model Development 1. Model-Development.ipynb

    2. *Practice_Model_Development_Laptops.ipynb*
  

• ✅ Model Evaluation & Refinement 1. Model_Evaluation_and_Refinement_cars.ipynb

    2. *Practice_Model_Evaluation_Laptops.ipynb*
  

• 💼 Real-World Projects 1. Practice_Project_Medical_Insurance.ipynb

    2. *Practice_Loading Laptop_Pricing.ipynb*
  

🧠 Skills Covered

This repository demonstrates and practices the following key data analysis skills:

1. 📥 Data Importing & Cleaning: Handling missing values, formatting issues, and inconsistent data types.

2. 📊 Exploratory Data Analysis: Using pandas, matplotlib, and seaborn for summarizing and visualizing data.

3. 🔎 Feature Engineering: Creating new features and selecting important ones for modeling.

4. 📈 Model Development: Building Linear, Multiple, and Polynomial Regression models using scikit-learn.

5. 🧪 Model Evaluation: Assessing models with R² score, RMSE, MAE, and residual plots.

6. 🛠️ Practical Projects: Real-world applications in medical insurance prediction, laptop pricing, and house price modeling.

🔧 Tools & Libraries Used

• Python 3.x

• Pandas

• NumPy

• Matplotlib

• Seaborn

• Scikit-learn

• Jupyter Notebook

📌 How to Use Clone this repository:

Run the notebooks to explore the data analysis workflows end-to-end.

📚 When Data Analysis Hands You a Lapse... "You pivot, visualize, refine — and let Python make sense of the mess." 💡

📬 Contact Feel free to reach out for collaborations or questions!

📧 gulamrasool2026@gmail.com 🔗 LinkedIn 🐍 github