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Aspiring Data Scientist | Machine Learning Enthusiast | Python
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Aspiring Data Scientist | Machine Learning Enthusiast | Python
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Sankesh12/README.md

Hi there 👋, I'm Sankesh Lal

AI & Data Science Enthusiast | Skilled in Python & Machine Learning | Aspiring Data Scientist

🧑‍💻 About Me

  • I specialize in turning raw data into actionable insights and building machine learning models that solve real-world problems.
  • I have hands-on experience in Python, data analysis. I am passionate about applying my skills to real-world problems, learning new technologies, and creating innovative solutions in AI and Data Science.

Career Goal: Seeking internship/full-time roles in Data Science & Machine Learning to contribute in impactful projects.

🛠 Skills & Tools

Programming Data Analysis Machine Learning Tools
Python 🐍 Pandas, NumPy, Matplotlib, Seaborn and EDA Scikit-learn, Regression & Classification Git, GitHub, Jupyter and VS Code

Currently Learning:

  • Advanced data analysis & visualization
  • Optimizing machine learning models
  • Feature engineering & model evaluation
  • End-to-end ML deployment workflows

🚀 Featured Projects

🎬 Movie Blockbuster Prediction

  • ML project to predict movie success using TMDB dataset.
  • Linear Regression, Random Forest, NLP (TF-IDF) and KMeans clustering.
  • Streamlit web app for real-time predictions.
  • 🔗 movie-blockbuster-prediction

💻 Laptop Price Predictor

  • Predict laptop prices using specs like RAM, Processor, Storage, GPU, OS.
  • Linear Regression, Random Forest, Gradient Boosting, XGBoost.
  • Interactive Streamlit web app.
  • 🔗 laptop-price-predictor

📧 Email/SMS Spam Classification

  • Classify messages as Spam or Not Spam using ML & TF-IDF.
  • Models: Naive Bayes, Logistic Regression, SVM, Random Forest, XGBoost.
  • Streamlit app for real-time spam detection.
  • 🔗 email/sms-spam-classification

🎥 Movie Review Sentiment Analysis

  • Predict sentiment of movie reviews (Positive/Negative) using ML & TF-IDF.
  • Logistic Regression, Naive Bayes, SVM, Random Forest.
  • Streamlit app for real-time predictions.
  • 🔗 movie-review-sentiment-analysis

🎓 Education & Certifications

  • Bachelors in Computer Science – Shah Abdul Latif University (2020–2023)
  • DIT (Diploma in IT) – SAKI Institute of Science & Technology, Sukkur (2020)

🌟 Interests & Soft Skills

  • Open-source AI/ML projects 🌐
  • Problem-solving, Collaboration, Creativity, Time-management, Adaptability and Communication 💡

📫 Contact:

📊 GitHub Stats

Sankesh's GitHub Stats

Popular repositories Loading

  1. House-Price-Prediction House-Price-Prediction Public

    🏠 Built a House Price Predictor. 🔎 Preprocessed housing data (handled missing values, log-transformed, encoded ocean proximity). 📊 Used features like rooms, bedrooms, population, households, income…

    Jupyter Notebook 1

  2. Movie-Review-Sentiment-Analysis Movie-Review-Sentiment-Analysis Public

    Built a system to predict if a movie review is Positive or Negative. 🎬 Used 50,000 IMDb reviews from Kaggle 💻 Converted text into numbers with TF-IDF Vectorizer 📊 Trained Logistic Regression, Naive…

    Jupyter Notebook

  3. Corporate-Data-Analyzer Corporate-Data-Analyzer Public

    A Python based desktop application that allows users to load Excel/CSV files, build group-wise reports using aggregations (Sum, Mean, Max, etc.), preview results in a table, visualize data with cha…

    Python

  4. Email-SMS-Spam-Classifier Email-SMS-Spam-Classifier Public

    Built an Email/SMS Spam Classifier using NLP 📧 Preprocessed text (tokenization, stopword removal, stemming) 🔎 Applied TF-IDF for feature extraction 📊 Trained multiple ML models and selected Multino…

    Jupyter Notebook

  5. Handwritten-Digit-Recognition Handwritten-Digit-Recognition Public

    A simple Machine Learning Tkinter app that lets you draw digits and predicts them using a trained SVM model.

    Jupyter Notebook

  6. Laptop-Price-Predictor Laptop-Price-Predictor Public

    A Machine Learning based web application that predicts laptop prices based on configuration details.

    Jupyter Notebook