Create README.md

README.md

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+🎬 Movie Revenue Prediction Project
+📈 Regression → Feature Engineering → Clustering → Classification → Model Deployment
+📦 Overview
+
+This project predicts movie revenue using both regression and classification models,
+powered by advanced feature engineering, clustering, and smart evaluation techniques.
+
+It was built as part of a Data Science assignment using the Movies Metadata dataset
+(Kaggle), processed and modeled in Google Colab.
+
+The final models are exported and published in a HuggingFace repository.
+
+🗂️ 1. Dataset
+
+Source: Kaggle’s Movies Metadata dataset
+
+Rows after cleaning: ~5,300
+
+Original target: revenue
+
+Classification target (later): revenue_class (high vs. low revenue)
+
+🔍 Main features used
+
+budget
+
+runtime
+
+vote_average
+
+vote_count
+
+popularity
+
+release_date → converted into release_year, decade
+
+overview → transformed into text length feature
+
+🧹 2. Data Cleaning & Preprocessing
+
+✔ Converted numeric fields to proper types
+✔ Removed impossible values (zero budget/revenue/runtime)
+✔ Parsed release_date into datetime
+✔ Handled missing values
+✔ Selected only meaningful rows for modeling
+
+📊 3. Exploratory Data Analysis
+📈 Budget vs Revenue
+
+Higher budget → generally higher revenue, though with big spread and outliers.
+
+⏱️ Runtime vs Revenue
+
+No strong linear trend, but most successful films fall within typical runtime (80–150 mins).
+
+🌍 Top Original Languages
+
+English overwhelmingly dominates the dataset.
+
+Each insight was supported by Matplotlib/Seaborn visualizations.
+
+🧱 4. Baseline Regression Model
+🎯 Goal
+
+Predict movie revenue using simple numeric features.
+
+🧩 Features
+
+budget, runtime, vote_average, vote_count
+
+⚙️ Model
+
+Linear Regression
+
+📐 Metrics
+
+MAE, MSE, RMSE, R²
+
+📝 Insight
+
+Good as a baseline, but not enough for real predictive power → motivates feature engineering.
+
+🛠️ 5. Feature Engineering
+
+Created new features:
+
+profit = revenue – budget
+
+profit_ratio = profit / budget
+
+overview_length (text length)
+
+release_year, decade
+
+Encoded categoricals (original_language, status)
+
+Standardized numeric features using StandardScaler
+
+Added cluster-based features from K-Means:
+
+cluster_group
+
+distance_to_centroid
+
+This significantly improved model learning capabilities.
+
+🎯 6. Clustering (K-Means + PCA)
+🤖 Unsupervised Learning
+
+K-Means with k = 4
+
+Features: budget, runtime, vote stats, popularity, profit
+
+🌀 PCA Visualization
+
+2D scatter plot revealing structured groups:
+
+Low-budget films
+
+Mid-tier films
+
+High-budget blockbusters
+
+Clusters later used as new predictive features.
+
+🚀 7. Improved Regression Models
+
+Trained 3 regression models:
+
+Linear Regression (improved)
+
+Random Forest Regressor
+
+Gradient Boosting Regressor ← 🏆 Winner
+
+🏆 Winning Model
+
+Gradient Boosting Regressor
+
+Why?
+
+Best R²
+
+Lowest MAE & RMSE
+
+Handles non-linear relationships beautifully
+
+Exported as:
+winning_model.pkl
+
+🔄 8. Regression → Classification
+
+The regression target was reframed into a binary classification problem:
+
+🎚️ Creating revenue_class
+
+Median split
+
+Class 0 → below median
+
+Class 1 → at or above median
+
+⚖️ Class Balance
+
+Perfectly balanced (~50/50).
+
+🧠 Business Reasoning
+
+Precision is more important than recall
+
+False Positives are more dangerous than False Negatives
+Predicting a movie as high-revenue when it won’t be → wastes millions.
+
+🤖 9. Classification Models
+
+Trained 3 classifiers:
+
+Logistic Regression
+
+Random Forest Classifier
+
+Gradient Boosting Classifier ← 🏆 Winner
+
+🧪 Metrics Evaluated:
+
+Accuracy
+
+Precision
+
+Recall
+
+F1-score
+
+Classification report
+
+Confusion matrix
+
+🏆 Winning Model: Gradient Boosting Classifier
+
+Highest precision (0.990)
+
+Highest F1-score (0.990)
+
+Lowest rate of harmful errors
+
+Exported as:
+winning_classifier.pkl