🧠 Machine Learning Projects

A curated portfolio of my Machine Learning projects focused on Regression, Classification, Clustering, and Sentiment Analysis using Python, Scikit-Learn, Pandas, NumPy, and essential ML techniques.

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📈 Regression Projects

🔹 Regression Analysis to Predict the Price of the Area
Predicts area pricing using linear regression and feature correlation.
🔗 https://github.com/snoobe838/Regression-Analysis-to-predict-the-price-of-the-area

🔹 Regression Analysis to Predict the Price by Inputs for an Area
Multi-variable regression model for predicting market value based on multiple input features.
🔗 https://github.com/snoobe838/Regression-Analysis-to-predict-the-price-by-the-inputs-for-a-Area

🔹 Placement Salary Estimation
Predicts salary based on academic score and experience using regression algorithms.
🔗 https://github.com/snoobe838/Placement-salary-estimation

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🧪 Classification & Machine Learning Projects

🔹 Survival Prediction using Classification Analysis
Predicts passenger survival using ML classifiers like Logistic Regression, Decision Trees, and SVM.
🔗 https://github.com/snoobe838/Supervised-Classification-Analysis-to-predict-the-survival

🔹 SMS & Email Spam Classifier
Machine learning model that classifies SMS and emails as spam or ham using NLP feature extraction (TF-IDF, Bag-of-Words).
🔗 https://github.com/snoobe838/SMS-Email_Spam_classifier

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🗂️ NLP & Sentiment Analysis Projects

🔹 Review Classification using Sentiment Analysis
NLP-based text classification model to detect positive and negative reviews using preprocessing and ML classifiers.
🔗 https://github.com/snoobe838/Reviews-classificatio-using-sentimental-analysis

🔹 SMS & Email Spam Classifier (also relevant here)
Text classification model using TF-IDF vectorization and supervised ML models to detect spam messages.
🔗 https://github.com/snoobe838/SMS-Email_Spam_classifier

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📊 Clustering Projects

🔹 K-Means Cluster Analysis for Customer Segmentation
Segments customers as potential targets using K-Means clustering.
🔗 https://github.com/snoobe838/K-Means-Cluster-Analysis-to-predict-Customer-is-target-or-not

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🔧 Tech Stack Used

🔸 Python
🔸 Scikit-Learn, Pandas, NumPy
🔸 NLTK, TextBlob, TF-IDF, Bag-of-Words
🔸 Matplotlib, Seaborn
🔸 Google Colab, Jupyter Notebook

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🚀 Key Machine Learning Concepts Applied

✔ Linear & Multiple Regression
✔ Classification (Logistic Regression, Decision Trees, SVM, Naive Bayes)
✔ K-Means Clustering
✔ NLP & TF-IDF, Bag-of-Words
✔ Sentiment & Spam Detection
✔ Feature Engineering
✔ Evaluation Metrics (Accuracy, RMSE, Precision, Recall, Silhouette Score)
✔ Exploratory Data Analysis (EDA)

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📬 Connect with Me

👤 Shubham Pareek
🌐 GitHub: https://github.com/snoobe838

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