🐍 Python Data Analysis Project

📌 Project Overview

This project demonstrates data preprocessing, exploratory data analysis (EDA), and predictive modeling using Python.
The workflow includes data cleaning, visualization, feature engineering, and machine learning models to extract insights and build decision-support systems.

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🎯 Objectives

• Import and preprocess dataset(s)
• Handle missing values, outliers, and skewness
• Perform EDA using descriptive statistics & visualizations
• Build predictive models (Regression / Classification / Clustering depending on dataset)
• Evaluate models with appropriate metrics
• Provide business insights and recommendations

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📂 Dataset

• Source: Provided dataset (CSV/Excel)
• Key features analyzed:
  • Demographics / transaction-related columns
  • Time/date fields for trend analysis
  • Target variable(s): cnt (bike rentals) / SalePrice (housing) / other depending on assignment

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🔎 Exploratory Data Analysis (EDA)

• 📊 Distribution plots for continuous variables
• 🗂️ Value counts for categorical features
• 📉 Outlier detection (Boxplots, IQR)
• 📈 Correlation heatmaps to detect multicollinearity

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🛠️ Data Preprocessing

• Removed duplicates & irrelevant columns
• Encoded categorical variables (Label / One-Hot Encoding)
• Scaled numerical features (MinMaxScaler / StandardScaler)
• Engineered features like seasonality, weather categories, comfort index, weekend/weekday

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🤖 Modeling & Machine Learning

• Algorithms applied:

  • ✅ Regression → predict continuous outcomes
  • ✅ Classification → label high/low value customers (Decision Trees, Logistic Regression)
  • ✅ Clustering → group customers into meaningful segments

• Evaluation Metrics:

  • Regression: R², RMSE
  • Classification: Accuracy, Precision, Recall, F1
  • Clustering: Silhouette Score

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📊 Results & Insights

• Clear seasonal trends (e.g., summer peaks for bike rentals, economic downturns affecting housing)
• Weather & working days strongly correlated with demand
• Predictive models achieved:
  • Regression: R² ~ 0.75–0.80
  • Classification: Accuracy ~ 82%
  • Clustering: Silhouette Score ~ 0.60+

## 👨‍💻 Tech Stack  
- **Python** → Pandas, NumPy, Matplotlib, Seaborn, Scikit-Learn  
- **Jupyter Notebook** → Analysis & Documentation  
- **Power BI / Tableau** (optional) → Dashboards  

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## ✅ Conclusion  
This project highlights how **Python-based analytics** transforms raw data into **actionable insights**.  
By combining **EDA, preprocessing, and predictive models**, the workflow supports smarter business decision-making in domains like:  
- 🚴 **Bike Sharing demand forecasting**  
- 🏡 **Housing price prediction**  
- 🚗 **Customer segmentation & marketing analytics**  

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