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fahadabid545/README.md

Hi 👋 I am Fahad

🎯 Aspiring Data Scientist | Machine Learning & NLP Enthusiast

📊 Passionate about Data Analytics & Business Intelligence

💡 Currently exploring ML Ops, Deep Learning, and Natural Language Processing

📫 Mail me at: fahadabid545@gmail.com

🌐 Connect with Me:

LinkedIn
LinkedIn 		Kaggle
Kaggle 		Instagram
Instagram

Tech Stack

Python R Django FastAPI MongoDB MySQL SQLite Matplotlib NumPy Pandas Plotly scikit-learn GitHub Git GitHub Actions Postman Power Bi

Git Stats


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  1. Creating-Pipeline Creating-Pipeline Public

    A complete machine learning pipeline using Decision Tree Regression with preprocessing transformations. The model is serialized using Pickle, enabling easy reuse for predictions on new data.

    Jupyter Notebook

  2. EDA-Analysis EDA-Analysis Public

    In-depth EDA to uncover patterns, trends, and insights using visualizations and statistical summaries. Includes data cleaning, feature understanding, and correlation analysis.

    Jupyter Notebook

  3. ElasticNet-Regression ElasticNet-Regression Public

    A Python implementation of ElasticNet Regression, combining L1 and L2 regularization to improve model performance and feature selection on random generated datasets

    Jupyter Notebook

  4. Pandas-Profiling Pandas-Profiling Public

    Automated EDA (Exploratory Data Analysis) using Pandas Profiling to generate interactive reports for quick insights into dataset structure, missing values, correlations, and distrib

    HTML

  5. Task-with-Flask Task-with-Flask Public

    TaskManager-Flask is a simple web-based task management application built with Flask and SQLAlchemy. The app uses SQLite as the database to store tasks, and each task has an associated creation dat…

    HTML

  6. PCA-Implementation PCA-Implementation Public

    An implementation of Principal Component Analysis (PCA) for dimensionality reduction on the MNIST dataset. This project reduces the feature space and visualizes the impact of PCA on classification …

    Jupyter Notebook

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