Swagat hai! Agar aap Machine Learning ki duniya mein kadam rakh rahe hain, toh ye repo aapke liye hai. Maine yahan Real-World Datasets ka use karke basic ML programs ko bahut hi simple tareeke se rakha hai.
Beginners ko ML ke complex concepts (Classifiers, Vectorizers, Accuracy) ko asaan code aur real data ke saath samjhana. Har file ek complete "Project" hai.
| Sr. No | Project Name | Topic Covered | Dataset |
|---|---|---|---|
| 1 | Spam Detector 🚨 | Text Classification, TF-IDF | SMS Spam Collection |
| 2 | Iris Classifier 🌸 | Multi-class Classification | Iris Flower Dataset |
| 3 | House Price Predictor 🏠 | Linear Regression | Boston/California Housing |
| 4 | Customer Segmentation 👥 | Clustering (K-Means) | Mall Customer Data |
- Language: Python 🐍
- Library: Scikit-Learn (sklearn)
- Data Tools: Pandas, Numpy
- Visualization: Matplotlib, Seaborn
- Data ko Clean kaise karte hain.
- Text ko Numbers mein kaise badalte hain (Tfidf/Label Encoding).
- Imbalanced Data (90:10 ratio) ko balance kaise banate hain.
- Model ki Accuracy, Precision aur Recall kaise check karte hain.
- Repo Clone karein:
git clone https://github.com
Required Libraries install karein:
pip install scikit-learn pandas numpy matplotlib seaborn
Koi bhi .py file run karein aur result dekhein!
🤝 Contributing Agar aapke paas koi simple ML code hai jo beginners ki help kar sake, toh Pull Request zaroor bhejein. Hamein milkar seekhna hai! 💡 ⭐ Support Agar ye repo aapke kaam aayi, toh ek Star 🌟 dekar support dikhayein!
Har file ke andar (Code ke upar) Comments zaroor likhein, jaise:
# Step 1: Loading Data# Step 2: Cleaning Data# Step 3: Training Model
Beginners ke liye ye comments kisi khazane se kam nahi hote.