✅ README 1 — Online News Popularity (Shares Prediction)
Domain: Social Media Goal: Predict whether an article will be popular (shares ≥ 1400) or unpopular.
Project Overview
Mashable collected metadata for ~40,000 online articles. The task is to build a classification model that predicts if an article will go viral based on its content features, sentiment, metadata, keywords, and publishing time.
Dataset
File: OnlineNewsPopularity.csv Rows: ~39,644 Features: 60+ Includes:
Content statistics (title length, word count, token stats)
Sentiment polarity metrics
Channel/category indicators
Day-of-week publishing flags
Keyword & link metadata
Target: shares
Articles were labeled as:
1 → shares ≥ 1400 (popular)
0 → shares < 1400 (unpopular)
Key Steps
- Data Exploration
Checked dataset structure using .info(), .describe(), .head()
Built a correlation heatmap
Visualized publishing day vs popularity
Visualized article categories vs popularity distribution
- Feature Engineering
Log-transform applied to skewed numerical columns
Kept sentiment-related features unchanged
Dropped unsupported columns (URL, timedelta)
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Train–Test Split train_test_split(data[features], data['shares'], test_size=0.25, random_state=0)
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Models Trained
Random Forest Classifier
Bernoulli Naive Bayes
k-Nearest Neighbors
Support Vector Machine (RBF kernel)
Results Model Accuracy Random Forest 0.6649 KNN 0.6070 Naive Bayes 0.5924 SVM (RBF, probability=True) ~0.63 (varies by run)
Conclusion: Random Forest offers the best performance without heavy tuning.
Tools & Libraries
Python
pandas, numpy
seaborn, matplotlib
scikit-learn
Next Improvements
Hyperparameter tuning (GridSearchCV)
XGBoost for higher accuracy
Balanced dataset handling
Advanced NLP-based embeddings (TF-IDF on title/content)
✅ README 2 — Avazu / CTR Prediction Project (Your First Code Block)
Domain: Digital Advertising / Machine Learning Goal: Predict the click-through rate (CTR) of ads using historical ad features.
Project Overview
The objective is to predict whether an ad will be clicked based on categorical and numerical attributes available in the ad logs.
Dataset
Input: Training CSV with features describing ad interactions
Target: click (0 = no click, 1 = clicked)
Dataset characteristics:
Large volume
Mostly categorical
Typical for CTR prediction challenges
Key Steps
- Data Preparation
Loaded training and test data using pandas
Encoded target labels using LabelEncoder
Selected numerical and categorical columns for modeling
Converted categories with appropriate encoding
- Model Training
Evaluated four ML algorithms:
RandomForestClassifier
DecisionTreeClassifier
NaiveBayes
Support Vector Machine (SVC)
- Evaluation
Metrics used:
Accuracy
Log Loss
Workflow:
model.predict(X_test) model.predict_proba(X_test) log_loss(y_test, pred)
- Best Model
Random Forest achieved the highest accuracy and lower log-loss compared to other models.
Tools Used
pandas, numpy
scikit-learn (RF, SVM, NB, DT)
matplotlib
Future Enhancements
One-hot encoding for high-cardinality categories
Feature hashing
XGBoost or LightGBM
Hyperparameter tuning
Calibration to improve predicted probabilities
✅ README 3 — Salary Prediction Using Supervised Learning
Domain: Human Resources / ML Regression Goal: Predict employee salary based on skills, experience, industry, and demographic features.
Project Overview
This project builds a regression model to estimate a professional’s salary using structured input features. The work includes data cleaning, visualization, model building, and evaluation.
Dataset
Contains:
Education details
Job title / seniority
Experience
Location
Industry
Salary (target variable)
Key Steps
- Data Cleaning
Removed missing or inconsistent entries
Encoded categorical features
Normalized numerical variables
- Exploratory Data Analysis (EDA)
Correlation heatmap
Boxplots and distribution visualizations
Outlier detection
Trends by role, experience and education
- Modeling
Trained multiple regression models:
Linear Regression
Decision Tree Regressor
Random Forest Regressor
Support Vector Regression (SVR)
Evaluated using:
MAE
RMSE
R² Score
- Best Model
Random Forest Regressor produced the most stable performance with lowest error.
Libraries Used
pandas
numpy
seaborn, matplotlib
scikit-learn
Possible Improvements
Hyperparameter tuning
Cross-validation
Feature selection based on SHAP
Using CatBoost or LightGBM
If you'd like, I can also:
✅ Combine all 3 into a single portfolio README ✅ Add GitHub folder structure ✅ Generate badges (accuracy, dataset size, language) ✅ Create a polished "Project Summary" for LinkedIn
Just tell me!