☁️ Indian Weather Prediction Project

---

📌 Project Overview

This project focuses on predicting key weather parameters in India — including temperature_celsius, feels_like_celsius, and cloud coverage — using machine learning models. It leverages a rich dataset from the Indian Weather Repository to train and evaluate a suite of regression algorithms.

---

🛠 Key Features

• Data preprocessing: cleaning, feature selection, and scaling
• Visual exploration with histograms, heatmaps, scatter plots
• Machine learning models: Linear, Ridge, Lasso, Random Forest, Gradient Boosting, XGBoost
• Evaluation using R², MSE, RMSE, MAE
• K-Fold Cross Validation for performance robustness

---

🧠 How It Works

1. Load dataset (IndianWeatherRepository.csv)
2. Clean and preprocess data
3. Visualize data relationships and trends
4. Split dataset into train/test sets
5. Scale features with StandardScaler
6. Train multiple regression models
7. Evaluate model accuracy and visualize predictions

---

📁 Project Files

File Name	Description
IndianWeatherRepository.ipynb	Main Python notebook
IndianWeatherRepository.csv	Weather dataset
histogram.png	Feature distribution histograms
correlation.png	Correlation heatmap
air_quality_vs_temp.png	Air quality vs. temperature scatter plot
newplot1.png - newplot5.jpg	Additional visualizations (sunburst, polar, etc.)

---

---

🔗 Dataset Access

The dataset used in this project is publicly available on Kaggle:

📂 Indian Weather Repository – Daily Snapshot

ℹ️ After downloading, rename the file to IndianWeatherRepository.csv and place it in the same folder as your script before running the project.

---

▶️ How to Run

1. Install required libraries:

pip install pandas matplotlib seaborn plotly scikit-learn xgboost

2. Ensure the dataset CSV is in the same folder as the script.

3. Run the script:

python IndianWeatherRepository.py

---

📈 Results Summary

🔸 Temperature (°C)

Model	R²	MSE	RMSE	MAE
Linear Regression	0.72	10.79	3.28	2.56
Lasso	0.72	10.82	3.29	2.56
Ridge	0.72	10.79	3.28	2.56
Gradient Boosting	0.86	5.55	2.35	1.80
Random Forest	0.93	2.60	1.61	1.08
XGBoost	0.91	3.44	1.85	1.37

✅ Best performer: Random Forest

🔸 Feels Like Temperature (°C)

Model	R²	MSE	RMSE	MAE
Linear Regression	0.74	14.25	3.77	2.94
Lasso	0.74	14.29	3.78	2.94
Ridge	0.74	14.25	3.77	2.94
Gradient Boosting	0.86	7.84	2.80	2.16
Random Forest	0.93	3.64	1.91	1.31
XGBoost	0.91	4.73	2.18	1.64

✅ Best performer: Random Forest

---

📊 Sample Visualizations

Feature Distributions

Correlation Heatmap

Temperature vs Air Quality

---

🚀 Future Improvements

• Implement LSTM/GRU for time-series prediction
• Build a real-time web dashboard (e.g., Flask + Streamlit)
• Incorporate external APIs or satellite weather feeds
• Use deep feature engineering for seasonal effects
• Deploy as a cloud-based ML service

---

👤 Author

Name: Arshdeep Yadav
Department: B.Tech Computer Science & Engineering
Institution: R.E.C. Kannauj