Added a mini machine learning project: Titanic Survival Prediction

Domains/AI-ML/MiniProjects/TitanicSurvivalPrediction/README.md

@@ -0,0 +1,44 @@
+## 📌 Overview
+This project applies **Machine Learning** techniques to predict passenger survival on the **Titanic** dataset.  
+The model is trained using **RandomForest + GridSearchCV**, achieving a **Kaggle score of 0.77751 (Top 22%)**.
+
+---
+
+## 📊 Dataset Information
+| Feature          | Description                           |
+|------------------|---------------------------------------|
+| PassengerId      | Unique ID of each passenger           |
+| Pclass           | Ticket class (1 = 1st, 2 = 2nd, 3 = 3rd) |
+| Name             | Passenger name                        |
+| Sex              | Gender                                |
+| Age              | Age in years                          |
+| SibSp            | # of siblings/spouses aboard          |
+| Parch            | # of parents/children aboard          |
+| Ticket           | Ticket number                         |
+| Fare             | Passenger fare                        |
+| Cabin            | Cabin number                          |
+| Embarked         | Port of Embarkation (C, Q, S)         |
+| Survived (Target)| 0 = No, 1 = Yes                       |
+
+- **Samples:** 891 (train) + 418 (test)  
+- **Classes:** 2 (Survived, Not Survived)  
+- **Source:** [Kaggle Titanic Dataset](https://www.kaggle.com/c/titanic)
+
+---
+
+## 🖼️ Visualizations
+
+1️⃣ Survival based on Sex
+<p align="center">
+  <img src="eda_survival_by_sex.png" width="80%">
+</p>
+
+2️⃣ Survival based on Passenger Class
+<p align="center">
+  <img src="eda_survival_by_pclass.png" width="80%">
+</p>
+
+3️⃣ Random Forest Feature Importance
+<p align="center">
+  <img src="rf_feature_importances.png" width="80%">
+</p>

Domains/AI-ML/MiniProjects/TitanicSurvivalPrediction/main.py

@@ -0,0 +1,129 @@
+# =============================================================================
+# Titanic - GridSearchCV RandomForest + Submission
+# =============================================================================
+
+import pandas as pd
+import seaborn as sns
+import matplotlib.pyplot as plt
+
+from sklearn.model_selection import train_test_split, GridSearchCV
+from sklearn.ensemble import RandomForestClassifier
+from sklearn.metrics import accuracy_score
+from sklearn.preprocessing import LabelEncoder
+
+# -----------------------------------------------------------------------------
+# 1) Load data
+train_df = pd.read_csv('train.csv')
+test_df  = pd.read_csv('test.csv')
+
+print(train_df.head())
+print(train_df.info())
+
+# -----------------------------------------------------------------------------
+# (Optional) Quick EDA - SAVE PLOTS
+# -----------------------------------------------------------------------------
+
+# Survival by Sex
+sns.countplot(x='Survived', hue='Sex', data=train_df)
+plt.title("Survival by Sex")
+plt.savefig("eda_survival_by_sex.png", dpi=300, bbox_inches='tight')
+plt.close()
+
+# Survival by Passenger Class
+sns.countplot(x='Survived', hue='Pclass', data=train_df)
+plt.title("Survival by Passenger Class")
+plt.savefig("eda_survival_by_pclass.png", dpi=300, bbox_inches='tight')
+plt.close()
+
+# -----------------------------------------------------------------------------
+# 2) Cleaning
+# -----------------------------------------------------------------------------
+
+train_df['Age'].fillna(train_df['Age'].median(), inplace=True)
+test_df['Age'].fillna(test_df['Age'].median(), inplace=True)
+
+train_df['Embarked'].fillna(train_df['Embarked'].mode()[0], inplace=True)
+test_df['Embarked'].fillna(test_df['Embarked'].mode()[0], inplace=True)
+
+test_df['Fare'].fillna(test_df['Fare'].median(), inplace=True)
+
+for df in (train_df, test_df):
+    if 'Cabin' in df.columns:
+        df.drop(columns=['Cabin'], inplace=True)
+
+encoder = LabelEncoder()
+for col in ['Sex', 'Embarked']:
+    train_df[col] = encoder.fit_transform(train_df[col])
+    test_df[col]  = encoder.transform(test_df[col])
+
+# -----------------------------------------------------------------------------
+# 3) Features / Target
+# -----------------------------------------------------------------------------
+features = ["Pclass", "Sex", "Age", "SibSp", "Parch", "Fare", "Embarked"]
+X = train_df[features]
+y = train_df["Survived"]
+test_X = test_df[features]
+
+# -----------------------------------------------------------------------------
+# 4) Train/Validation split
+# -----------------------------------------------------------------------------
+X_train, X_val, y_train, y_val = train_test_split(
+    X, y, test_size=0.2, random_state=42
+)
+
+# -----------------------------------------------------------------------------
+# 5) Best model: RandomForest + GridSearchCV
+# -----------------------------------------------------------------------------
+param_grid = {
+    'n_estimators': [100, 200, 300],
+    'max_depth': [5, 10, None],
+    'min_samples_split': [2, 5, 10],
+    'max_features': ['sqrt', 'log2']
+}
+
+grid = GridSearchCV(
+    RandomForestClassifier(random_state=42),
+    param_grid=param_grid,
+    cv=3,
+    scoring='accuracy',
+    n_jobs=-1
+)
+
+grid.fit(X_train, y_train)
+
+print("Best Parameters:", grid.best_params_)
+print("Best CV Score:", grid.best_score_)
+
+# Validation score using the best estimator found by CV
+best_rf_val_pred = grid.best_estimator_.predict(X_val)
+print("Validation Accuracy (best RF):", accuracy_score(y_val, best_rf_val_pred))
+
+# -----------------------------------------------------------------------------
+# 6) Final training on ALL data + Submission
+# -----------------------------------------------------------------------------
+best_rf = grid.best_estimator_
+best_rf.fit(X, y)  # retrain on the full training data
+
+test_preds = best_rf.predict(test_X)
+
+submission = pd.DataFrame({
+    "PassengerId": test_df["PassengerId"],
+    "Survived": test_preds
+})
+submission.to_csv("submission.csv", index=False)
+print("submission.csv created ✅")
+
+# quick sanity checks
+print(submission.head())
+print(submission['Survived'].value_counts())
+
+# -----------------------------------------------------------------------------
+# 7) Save Feature Importances Plot
+# -----------------------------------------------------------------------------
+importances = best_rf.feature_importances_
+features = X.columns
+
+sns.barplot(x=importances, y=features)
+plt.title("Feature Importances")
+plt.savefig("rf_feature_importances.png", dpi=300, bbox_inches='tight')
+plt.close()