Welcome! This repository is a collection of projects, self-learning notebooks, and notes that I created while exploring the world of machine learning and data analysis with Python.
Although I am primarily a Web Developer, I pursued this learning path to expand my skill set and understand how AI/ML can be integrated into modern web applications and data-driven platforms.
This is a separate project hosted in another repository:
🔗 ML Data Analysis Web App (Streamlit)
It is an interactive web application built with Streamlit that allows users to:
- ✅ Upload CSV datasets
- 📊 Visualize data with interactive charts (Seaborn, Matplotlib)
- 🧠 Train machine learning models (e.g., Logistic Regression, Decision Tree)
- 🧪 Evaluate model performance (accuracy, precision, etc.)
- 📥 Make predictions and download results
🌐 Live App: https://machinelearning-app.streamlit.app/
Hands-on machine learning projects with end-to-end workflows.
CreditCardApproval/: Classification model for predicting credit card approvalsHepatitus_project/: Model for diagnosing Hepatitis based on patient datasaved_models/: Serialized models for reuse (.pkl)
Beginner-to-intermediate notebooks focused on understanding ML concepts and data preprocessing.
| Notebook | Focus |
|---|---|
| 1_handling_missing_value.ipynb | Handling missing data in datasets |
| 2_handling_inconsistancy.ipynb | Data cleaning for inconsistencies |
| 3_handling_outlier.ipynb | Detecting and handling outliers |
| 4_data_merging.ipynb | Combining datasets |
| 5_scaling.ipynb | Feature scaling techniques |
| 6_encoding.ipynb | Encoding categorical variables |
| Decision_Tree.ipynb | Decision Tree classifier |
| svm.ipynb | Support Vector Machine model |
| linear_regression*.ipynb | Linear regression examples |
| logistic_regression*.ipynb | Binary classification using Logistic Regression |
| polynomial_regression.ipynb | Polynomial regression |
| ridge_regression.ipynb | Ridge regularization |
Quick reference and practice notebooks for Python basics and libraries:
numpy.ipynb: Numpy basicspandas.ipynb: Data manipulation with Pandasseaborn_matplotlib.ipynb: Data visualization tips
- Python
- Jupyter Notebook
- Streamlit
- scikit-learn
- pandas, NumPy
- Seaborn, Matplotlib
This repo is a part of my self-learning journey into AI and machine learning. While my core expertise is in web development, I believe that being a versatile developer means embracing data science and AI where needed.
I’m Fahad Ali, a full-stack web developer with a growing interest in data science and AI.
If you'd like to collaborate or discuss ideas, feel free to reach out on LinkedIn.