Initial titanicsurvival commit

.ipynb_checkpoints/notebook-checkpoint.ipynb

@@ -0,0 +1,178 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Libraries\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import seaborn as sns\n",
+    "import matplotlib.pyplot as plt\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "from sklearn.preprocessing import OneHotEncoder, StandardScaler\n",
+    "from sklearn.impute import SimpleImputer\n",
+    "from sklearn.ensemble import RandomForestClassifier\n",
+    "from sklearn.linear_model import LogisticRegression\n",
+    "from sklearn.metrics import accuracy_score, classification_report, confusion_matrix"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Load\n",
+    "def load_data():\n",
+    "    train_df = pd.read_csv(\"datasets/train.csv\")\n",
+    "    test_df = pd.read_csv(\"datasets/test.csv\")\n",
+    "    return train_df, test_df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Preprocess\n",
+    "def preprocess_data(df, is_train=True):\n",
+    "    df = df.copy()\n",
+    "    \n",
+    "    # Fill missing age values with median\n",
+    "    age_imputer = SimpleImputer(strategy=\"median\")\n",
+    "    df[\"Age\"] = age_imputer.fit_transform(df[[\"Age\"]])\n",
+    "    \n",
+    "    # Fill missing embarked values with mode\n",
+    "    embarked_imputer = SimpleImputer(strategy=\"most_frequent\")\n",
+    "    df[\"Embarked\"] = embarked_imputer.fit_transform(df[[\"Embarked\"]])\n",
+    "    \n",
+    "    # Convert categorical to numerical\n",
+    "    df[\"Sex\"] = df[\"Sex\"].map({\"male\": 0, \"female\": 1})\n",
+    "    df = pd.get_dummies(df, columns=[\"Embarked\"], drop_first=True)\n",
+    "    \n",
+    "    # Create new features\n",
+    "    df[\"FamilySize\"] = df[\"SibSp\"] + df[\"Parch\"] + 1\n",
+    "    df.drop([\"Name\", \"Ticket\", \"Cabin\", \"PassengerId\"], axis=1, inplace=True)\n",
+    "    \n",
+    "    return df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Training\n",
+    "def train_model(X_train, y_train):\n",
+    "    model = RandomForestClassifier(n_estimators=100, random_state=42)\n",
+    "    model.fit(X_train, y_train)\n",
+    "    return model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Evaluation\n",
+    "def evaluate_model(model, X_val, y_val):\n",
+    "    y_pred = model.predict(X_val)\n",
+    "    print(\"Accuracy:\", accuracy_score(y_val, y_pred))\n",
+    "    print(\"Classification Report:\\n\", classification_report(y_val, y_pred))\n",
+    "    sns.heatmap(confusion_matrix(y_val, y_pred), annot=True, fmt=\"d\", cmap=\"Blues\")\n",
+    "    plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "ValueError",
+     "evalue": "2",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
+      "Cell \u001b[1;32mIn[20], line 3\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[38;5;66;03m# Exec\u001b[39;00m\n\u001b[0;32m      2\u001b[0m train_df, test_df \u001b[38;5;241m=\u001b[39m load_data()\n\u001b[1;32m----> 3\u001b[0m train_df \u001b[38;5;241m=\u001b[39m \u001b[43mpreprocess_data\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtrain_df\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m      4\u001b[0m test_df \u001b[38;5;241m=\u001b[39m preprocess_data(test_df, is_train\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m)\n",
+      "Cell \u001b[1;32mIn[17], line 11\u001b[0m, in \u001b[0;36mpreprocess_data\u001b[1;34m(df, is_train)\u001b[0m\n\u001b[0;32m      9\u001b[0m \u001b[38;5;66;03m# Fill missing embarked values with mode\u001b[39;00m\n\u001b[0;32m     10\u001b[0m embarked_imputer \u001b[38;5;241m=\u001b[39m SimpleImputer(strategy\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmost_frequent\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m---> 11\u001b[0m \u001b[43mdf\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mEmbarked\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m \u001b[38;5;241m=\u001b[39m embarked_imputer\u001b[38;5;241m.\u001b[39mfit_transform(df[[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mEmbarked\u001b[39m\u001b[38;5;124m\"\u001b[39m]])\n\u001b[0;32m     13\u001b[0m \u001b[38;5;66;03m# Convert categorical to numerical\u001b[39;00m\n\u001b[0;32m     14\u001b[0m df[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mSex\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m df[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mSex\u001b[39m\u001b[38;5;124m\"\u001b[39m]\u001b[38;5;241m.\u001b[39mmap({\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmale\u001b[39m\u001b[38;5;124m\"\u001b[39m: \u001b[38;5;241m0\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mfemale\u001b[39m\u001b[38;5;124m\"\u001b[39m: \u001b[38;5;241m1\u001b[39m})\n",
+      "File \u001b[1;32mc:\\Users\\Lenovo\\Desktop\\Github\\CCS-229\\venv\\Lib\\site-packages\\pandas\\core\\frame.py:4311\u001b[0m, in \u001b[0;36mDataFrame.__setitem__\u001b[1;34m(self, key, value)\u001b[0m\n\u001b[0;32m   4308\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_setitem_array([key], value)\n\u001b[0;32m   4309\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m   4310\u001b[0m     \u001b[38;5;66;03m# set column\u001b[39;00m\n\u001b[1;32m-> 4311\u001b[0m     \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_set_item\u001b[49m\u001b[43m(\u001b[49m\u001b[43mkey\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mvalue\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[1;32mc:\\Users\\Lenovo\\Desktop\\Github\\CCS-229\\venv\\Lib\\site-packages\\pandas\\core\\frame.py:4524\u001b[0m, in \u001b[0;36mDataFrame._set_item\u001b[1;34m(self, key, value)\u001b[0m\n\u001b[0;32m   4514\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21m_set_item\u001b[39m(\u001b[38;5;28mself\u001b[39m, key, value) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m   4515\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m   4516\u001b[0m \u001b[38;5;124;03m    Add series to DataFrame in specified column.\u001b[39;00m\n\u001b[0;32m   4517\u001b[0m \n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m   4522\u001b[0m \u001b[38;5;124;03m    ensure homogeneity.\u001b[39;00m\n\u001b[0;32m   4523\u001b[0m \u001b[38;5;124;03m    \"\"\"\u001b[39;00m\n\u001b[1;32m-> 4524\u001b[0m     value, refs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_sanitize_column\u001b[49m\u001b[43m(\u001b[49m\u001b[43mvalue\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m   4526\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m (\n\u001b[0;32m   4527\u001b[0m         key \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcolumns\n\u001b[0;32m   4528\u001b[0m         \u001b[38;5;129;01mand\u001b[39;00m value\u001b[38;5;241m.\u001b[39mndim \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m1\u001b[39m\n\u001b[0;32m   4529\u001b[0m         \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(value\u001b[38;5;241m.\u001b[39mdtype, ExtensionDtype)\n\u001b[0;32m   4530\u001b[0m     ):\n\u001b[0;32m   4531\u001b[0m         \u001b[38;5;66;03m# broadcast across multiple columns if necessary\u001b[39;00m\n\u001b[0;32m   4532\u001b[0m         \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcolumns\u001b[38;5;241m.\u001b[39mis_unique \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcolumns, MultiIndex):\n",
+      "File \u001b[1;32mc:\\Users\\Lenovo\\Desktop\\Github\\CCS-229\\venv\\Lib\\site-packages\\pandas\\core\\frame.py:5267\u001b[0m, in \u001b[0;36mDataFrame._sanitize_column\u001b[1;34m(self, value)\u001b[0m\n\u001b[0;32m   5265\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m is_list_like(value):\n\u001b[0;32m   5266\u001b[0m     com\u001b[38;5;241m.\u001b[39mrequire_length_match(value, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mindex)\n\u001b[1;32m-> 5267\u001b[0m arr \u001b[38;5;241m=\u001b[39m \u001b[43msanitize_array\u001b[49m\u001b[43m(\u001b[49m\u001b[43mvalue\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mindex\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcopy\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mallow_2d\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m)\u001b[49m\n\u001b[0;32m   5268\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m (\n\u001b[0;32m   5269\u001b[0m     \u001b[38;5;28misinstance\u001b[39m(value, Index)\n\u001b[0;32m   5270\u001b[0m     \u001b[38;5;129;01mand\u001b[39;00m value\u001b[38;5;241m.\u001b[39mdtype \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mobject\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m   5273\u001b[0m     \u001b[38;5;66;03m# TODO: Remove kludge in sanitize_array for string mode when enforcing\u001b[39;00m\n\u001b[0;32m   5274\u001b[0m     \u001b[38;5;66;03m# this deprecation\u001b[39;00m\n\u001b[0;32m   5275\u001b[0m     warnings\u001b[38;5;241m.\u001b[39mwarn(\n\u001b[0;32m   5276\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mSetting an Index with object dtype into a DataFrame will stop \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m   5277\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124minferring another dtype in a future version. Cast the Index \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m   5280\u001b[0m         stacklevel\u001b[38;5;241m=\u001b[39mfind_stack_level(),\n\u001b[0;32m   5281\u001b[0m     )\n",
+      "File \u001b[1;32mc:\\Users\\Lenovo\\Desktop\\Github\\CCS-229\\venv\\Lib\\site-packages\\pandas\\core\\construction.py:606\u001b[0m, in \u001b[0;36msanitize_array\u001b[1;34m(data, index, dtype, copy, allow_2d)\u001b[0m\n\u001b[0;32m    604\u001b[0m subarr \u001b[38;5;241m=\u001b[39m data\n\u001b[0;32m    605\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m data\u001b[38;5;241m.\u001b[39mdtype \u001b[38;5;241m==\u001b[39m \u001b[38;5;28mobject\u001b[39m:\n\u001b[1;32m--> 606\u001b[0m     subarr \u001b[38;5;241m=\u001b[39m \u001b[43mmaybe_infer_to_datetimelike\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdata\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    607\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m (\n\u001b[0;32m    608\u001b[0m         object_index\n\u001b[0;32m    609\u001b[0m         \u001b[38;5;129;01mand\u001b[39;00m using_pyarrow_string_dtype()\n\u001b[0;32m    610\u001b[0m         \u001b[38;5;129;01mand\u001b[39;00m is_string_dtype(subarr)\n\u001b[0;32m    611\u001b[0m     ):\n\u001b[0;32m    612\u001b[0m         \u001b[38;5;66;03m# Avoid inference when string option is set\u001b[39;00m\n\u001b[0;32m    613\u001b[0m         subarr \u001b[38;5;241m=\u001b[39m data\n",
+      "File \u001b[1;32mc:\\Users\\Lenovo\\Desktop\\Github\\CCS-229\\venv\\Lib\\site-packages\\pandas\\core\\dtypes\\cast.py:1181\u001b[0m, in \u001b[0;36mmaybe_infer_to_datetimelike\u001b[1;34m(value)\u001b[0m\n\u001b[0;32m   1178\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m(\u001b[38;5;28mtype\u001b[39m(value))  \u001b[38;5;66;03m# pragma: no cover\u001b[39;00m\n\u001b[0;32m   1179\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m value\u001b[38;5;241m.\u001b[39mndim \u001b[38;5;241m!=\u001b[39m \u001b[38;5;241m1\u001b[39m:\n\u001b[0;32m   1180\u001b[0m     \u001b[38;5;66;03m# Caller is responsible\u001b[39;00m\n\u001b[1;32m-> 1181\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(value\u001b[38;5;241m.\u001b[39mndim)  \u001b[38;5;66;03m# pragma: no cover\u001b[39;00m\n\u001b[0;32m   1183\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(value):\n\u001b[0;32m   1184\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m value\n",
+      "\u001b[1;31mValueError\u001b[0m: 2"
+     ]
+    }
+   ],
+   "source": [
+    "# Exec\n",
+    "train_df, test_df = load_data()\n",
+    "train_df = preprocess_data(train_df)\n",
+    "test_df = preprocess_data(test_df, is_train=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Split\n",
+    "X = train_df.drop(\"Survived\", axis=1)\n",
+    "y = train_df[\"Survived\"]\n",
+    "X_train, X_val, y_train, y_val = train_test_split(X, y, test_size=0.2, random_state=42)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Train/evaluate\n",
+    "model = train_model(X_train, y_train)\n",
+    "evaluate_model(model, X_val, y_val)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Test\n",
+    "test_predictions = model.predict(test_df)\n",
+    "pd.DataFrame({\"PassengerId\": test_df.index, \"Survived\": test_predictions}).to_csv(\"submission.csv\", index=False)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "venv",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

app.py

@@ -0,0 +1,83 @@
+import streamlit as st
+import pandas as pd
+import numpy as np
+import seaborn as sns
+import matplotlib.pyplot as plt
+from sklearn.ensemble import RandomForestClassifier
+from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score, confusion_matrix
+from sklearn.model_selection import train_test_split
+
+# Load
+train_df = pd.read_csv("Titanic-Survival/datasets/train.csv")
+
+# Preprocess
+train_df["Age"].fillna(train_df["Age"].median(), inplace=True)
+train_df["Embarked"].fillna(train_df["Embarked"].mode()[0], inplace=True)
+train_df["Sex"] = train_df["Sex"].map({"male": 0, "female": 1})
+train_df = pd.get_dummies(train_df, columns=["Embarked"], drop_first=True)
+train_df["FamilySize"] = train_df["SibSp"] + train_df["Parch"] + 1
+train_df.drop(["Name", "Ticket", "Cabin", "PassengerId"], axis=1, inplace=True)
+
+# Train
+X = train_df.drop("Survived", axis=1)
+y = train_df["Survived"]
+X_train, X_val, y_train, y_val = train_test_split(X, y, test_size=0.2, random_state=42)
+model = RandomForestClassifier(n_estimators=100, random_state=42)
+model.fit(X_train, y_train)
+y_pred = model.predict(X_val)
+
+# App
+st.title("Titanic Survival Prediction")
+
+# Model Assessment
+st.header("Model Performance")
+st.write(f"**Accuracy:** {accuracy_score(y_val, y_pred):.2f}")
+st.write(f"**Precision:** {precision_score(y_val, y_pred):.2f}")
+st.write(f"**Recall:** {recall_score(y_val, y_pred):.2f}")
+st.write(f"**F1 Score:** {f1_score(y_val, y_pred):.2f}")
+fig, ax = plt.subplots()
+sns.heatmap(confusion_matrix(y_val, y_pred), annot=True, fmt="d", cmap="Blues", ax=ax)
+st.pyplot(fig)
+
+# Predictor
+st.header("Predict Survival")
+
+# Preset Character Data
+def set_character(pclass, sex, age, sibsp, parch, fare, embarked):
+    st.session_state.pclass = pclass
+    st.session_state.sex = sex
+    st.session_state.age = age
+    st.session_state.sibsp = sibsp
+    st.session_state.parch = parch
+    st.session_state.fare = fare
+    st.session_state.embarked = embarked
+    
+with st.expander("Movie Characters"):
+    col1, col2 = st.columns(2)
+    with col1:
+        st.button("Rose DeWitt Bukater", on_click=set_character, args=(1, "Female", 17, 0, 1, 300.0, "S")) # Rose was 17, embarked from South Hampton, was in first class and paid $300 as an estimate of the average of the 1st class fare, maybe a slightly less luxurious cabin. . She was with her mother.
+        st.button("Jack Dawson", on_click=set_character, args=(3, "Male", 20, 0, 0, 8.05, "S")) # Jack was 20, embarked from South Hampton, was in third class and paid a random estimate of $8.05 as the bet for the poker game where he won is 3rd class ticket. Of course, he was alone.
+    with col2:
+        st.button("Caledon Hockley", on_click=set_character, args=(1, "Male", 30, 0, 0, 500.0, "C")) # Caledon was 30, embarked from Cherbourg, was in first class and paid $500 as an estimate of the high average of the 1st class fare. He was alone.
+        st.button("Ruth DeWitt Bukater", on_click=set_character, args=(1, "Female", 45, 0, 1, 300.0, "S")) # Ruth was 45, embarked from South Hampton, was in first class and paid $300 as an estimate of the average of the 1st class fare. She was with her daughter.
+
+pclass = st.selectbox("Passenger Class", [1, 2, 3], index=[1, 2, 3].index(st.session_state.get("pclass", 3)))
+sex = st.radio("Sex", ["Male", "Female"], index=["Male", "Female"].index(st.session_state.get("sex", "Male")))
+age = st.slider("Age", 0, 100, st.session_state.get("age", 30))
+sibsp = st.slider("Siblings/Spouses Aboard", 0, 8, st.session_state.get("sibsp", 0))
+parch = st.slider("Parents/Children Aboard", 0, 6, st.session_state.get("parch", 0))
+fare = st.number_input("Fare", 0.0, 500.0, st.session_state.get("fare", 30.0))
+embarked = st.selectbox("Port of Embarkation", ["C", "Q", "S"], index=["C", "Q", "S"].index(st.session_state.get("embarked", "S")))
+
+# Process Input
+sex = 1 if sex == "Female" else 0
+embarked_C = 1 if embarked == "C" else 0
+embarked_Q = 1 if embarked == "Q" else 0
+embarked_S = 1 if embarked == "S" else 0
+family_size = sibsp + parch + 1
+input_data = np.array([[pclass, sex, age, sibsp, parch, fare, family_size, embarked_C, embarked_Q]])
+
+# Predict and Output
+survival_probability = model.predict_proba(input_data)[0][1]
+st.subheader("Survival Probability")
+st.write(f"{survival_probability:.2%}")

datasets/test.csv

@@ -0,0 +1,419 @@
+PassengerId,Pclass,Name,Sex,Age,SibSp,Parch,Ticket,Fare,Cabin,Embarked
+892,3,"Kelly, Mr. James",male,34.5,0,0,330911,7.8292,,Q
+893,3,"Wilkes, Mrs. James (Ellen Needs)",female,47,1,0,363272,7,,S
+894,2,"Myles, Mr. Thomas Francis",male,62,0,0,240276,9.6875,,Q
+895,3,"Wirz, Mr. Albert",male,27,0,0,315154,8.6625,,S
+896,3,"Hirvonen, Mrs. Alexander (Helga E Lindqvist)",female,22,1,1,3101298,12.2875,,S
+897,3,"Svensson, Mr. Johan Cervin",male,14,0,0,7538,9.225,,S
+898,3,"Connolly, Miss. Kate",female,30,0,0,330972,7.6292,,Q
+899,2,"Caldwell, Mr. Albert Francis",male,26,1,1,248738,29,,S
+900,3,"Abrahim, Mrs. Joseph (Sophie Halaut Easu)",female,18,0,0,2657,7.2292,,C
+901,3,"Davies, Mr. John Samuel",male,21,2,0,A/4 48871,24.15,,S
+902,3,"Ilieff, Mr. Ylio",male,,0,0,349220,7.8958,,S
+903,1,"Jones, Mr. Charles Cresson",male,46,0,0,694,26,,S
+904,1,"Snyder, Mrs. John Pillsbury (Nelle Stevenson)",female,23,1,0,21228,82.2667,B45,S
+905,2,"Howard, Mr. Benjamin",male,63,1,0,24065,26,,S
+906,1,"Chaffee, Mrs. Herbert Fuller (Carrie Constance Toogood)",female,47,1,0,W.E.P. 5734,61.175,E31,S
+907,2,"del Carlo, Mrs. Sebastiano (Argenia Genovesi)",female,24,1,0,SC/PARIS 2167,27.7208,,C
+908,2,"Keane, Mr. Daniel",male,35,0,0,233734,12.35,,Q
+909,3,"Assaf, Mr. Gerios",male,21,0,0,2692,7.225,,C
+910,3,"Ilmakangas, Miss. Ida Livija",female,27,1,0,STON/O2. 3101270,7.925,,S
+911,3,"Assaf Khalil, Mrs. Mariana (Miriam"")""",female,45,0,0,2696,7.225,,C
+912,1,"Rothschild, Mr. Martin",male,55,1,0,PC 17603,59.4,,C
+913,3,"Olsen, Master. Artur Karl",male,9,0,1,C 17368,3.1708,,S
+914,1,"Flegenheim, Mrs. Alfred (Antoinette)",female,,0,0,PC 17598,31.6833,,S
+915,1,"Williams, Mr. Richard Norris II",male,21,0,1,PC 17597,61.3792,,C
+916,1,"Ryerson, Mrs. Arthur Larned (Emily Maria Borie)",female,48,1,3,PC 17608,262.375,B57 B59 B63 B66,C
+917,3,"Robins, Mr. Alexander A",male,50,1,0,A/5. 3337,14.5,,S
+918,1,"Ostby, Miss. Helene Ragnhild",female,22,0,1,113509,61.9792,B36,C
+919,3,"Daher, Mr. Shedid",male,22.5,0,0,2698,7.225,,C
+920,1,"Brady, Mr. John Bertram",male,41,0,0,113054,30.5,A21,S
+921,3,"Samaan, Mr. Elias",male,,2,0,2662,21.6792,,C
+922,2,"Louch, Mr. Charles Alexander",male,50,1,0,SC/AH 3085,26,,S
+923,2,"Jefferys, Mr. Clifford Thomas",male,24,2,0,C.A. 31029,31.5,,S
+924,3,"Dean, Mrs. Bertram (Eva Georgetta Light)",female,33,1,2,C.A. 2315,20.575,,S
+925,3,"Johnston, Mrs. Andrew G (Elizabeth Lily"" Watson)""",female,,1,2,W./C. 6607,23.45,,S
+926,1,"Mock, Mr. Philipp Edmund",male,30,1,0,13236,57.75,C78,C
+927,3,"Katavelas, Mr. Vassilios (Catavelas Vassilios"")""",male,18.5,0,0,2682,7.2292,,C
+928,3,"Roth, Miss. Sarah A",female,,0,0,342712,8.05,,S
+929,3,"Cacic, Miss. Manda",female,21,0,0,315087,8.6625,,S
+930,3,"Sap, Mr. Julius",male,25,0,0,345768,9.5,,S
+931,3,"Hee, Mr. Ling",male,,0,0,1601,56.4958,,S
+932,3,"Karun, Mr. Franz",male,39,0,1,349256,13.4167,,C
+933,1,"Franklin, Mr. Thomas Parham",male,,0,0,113778,26.55,D34,S
+934,3,"Goldsmith, Mr. Nathan",male,41,0,0,SOTON/O.Q. 3101263,7.85,,S
+935,2,"Corbett, Mrs. Walter H (Irene Colvin)",female,30,0,0,237249,13,,S
+936,1,"Kimball, Mrs. Edwin Nelson Jr (Gertrude Parsons)",female,45,1,0,11753,52.5542,D19,S
+937,3,"Peltomaki, Mr. Nikolai Johannes",male,25,0,0,STON/O 2. 3101291,7.925,,S
+938,1,"Chevre, Mr. Paul Romaine",male,45,0,0,PC 17594,29.7,A9,C
+939,3,"Shaughnessy, Mr. Patrick",male,,0,0,370374,7.75,,Q
+940,1,"Bucknell, Mrs. William Robert (Emma Eliza Ward)",female,60,0,0,11813,76.2917,D15,C
+941,3,"Coutts, Mrs. William (Winnie Minnie"" Treanor)""",female,36,0,2,C.A. 37671,15.9,,S
+942,1,"Smith, Mr. Lucien Philip",male,24,1,0,13695,60,C31,S
+943,2,"Pulbaum, Mr. Franz",male,27,0,0,SC/PARIS 2168,15.0333,,C
+944,2,"Hocking, Miss. Ellen Nellie""""",female,20,2,1,29105,23,,S
+945,1,"Fortune, Miss. Ethel Flora",female,28,3,2,19950,263,C23 C25 C27,S
+946,2,"Mangiavacchi, Mr. Serafino Emilio",male,,0,0,SC/A.3 2861,15.5792,,C
+947,3,"Rice, Master. Albert",male,10,4,1,382652,29.125,,Q
+948,3,"Cor, Mr. Bartol",male,35,0,0,349230,7.8958,,S
+949,3,"Abelseth, Mr. Olaus Jorgensen",male,25,0,0,348122,7.65,F G63,S
+950,3,"Davison, Mr. Thomas Henry",male,,1,0,386525,16.1,,S
+951,1,"Chaudanson, Miss. Victorine",female,36,0,0,PC 17608,262.375,B61,C
+952,3,"Dika, Mr. Mirko",male,17,0,0,349232,7.8958,,S
+953,2,"McCrae, Mr. Arthur Gordon",male,32,0,0,237216,13.5,,S
+954,3,"Bjorklund, Mr. Ernst Herbert",male,18,0,0,347090,7.75,,S
+955,3,"Bradley, Miss. Bridget Delia",female,22,0,0,334914,7.725,,Q
+956,1,"Ryerson, Master. John Borie",male,13,2,2,PC 17608,262.375,B57 B59 B63 B66,C
+957,2,"Corey, Mrs. Percy C (Mary Phyllis Elizabeth Miller)",female,,0,0,F.C.C. 13534,21,,S
+958,3,"Burns, Miss. Mary Delia",female,18,0,0,330963,7.8792,,Q
+959,1,"Moore, Mr. Clarence Bloomfield",male,47,0,0,113796,42.4,,S
+960,1,"Tucker, Mr. Gilbert Milligan Jr",male,31,0,0,2543,28.5375,C53,C
+961,1,"Fortune, Mrs. Mark (Mary McDougald)",female,60,1,4,19950,263,C23 C25 C27,S
+962,3,"Mulvihill, Miss. Bertha E",female,24,0,0,382653,7.75,,Q
+963,3,"Minkoff, Mr. Lazar",male,21,0,0,349211,7.8958,,S
+964,3,"Nieminen, Miss. Manta Josefina",female,29,0,0,3101297,7.925,,S
+965,1,"Ovies y Rodriguez, Mr. Servando",male,28.5,0,0,PC 17562,27.7208,D43,C
+966,1,"Geiger, Miss. Amalie",female,35,0,0,113503,211.5,C130,C
+967,1,"Keeping, Mr. Edwin",male,32.5,0,0,113503,211.5,C132,C
+968,3,"Miles, Mr. Frank",male,,0,0,359306,8.05,,S
+969,1,"Cornell, Mrs. Robert Clifford (Malvina Helen Lamson)",female,55,2,0,11770,25.7,C101,S
+970,2,"Aldworth, Mr. Charles Augustus",male,30,0,0,248744,13,,S
+971,3,"Doyle, Miss. Elizabeth",female,24,0,0,368702,7.75,,Q
+972,3,"Boulos, Master. Akar",male,6,1,1,2678,15.2458,,C
+973,1,"Straus, Mr. Isidor",male,67,1,0,PC 17483,221.7792,C55 C57,S
+974,1,"Case, Mr. Howard Brown",male,49,0,0,19924,26,,S
+975,3,"Demetri, Mr. Marinko",male,,0,0,349238,7.8958,,S
+976,2,"Lamb, Mr. John Joseph",male,,0,0,240261,10.7083,,Q
+977,3,"Khalil, Mr. Betros",male,,1,0,2660,14.4542,,C
+978,3,"Barry, Miss. Julia",female,27,0,0,330844,7.8792,,Q
+979,3,"Badman, Miss. Emily Louisa",female,18,0,0,A/4 31416,8.05,,S
+980,3,"O'Donoghue, Ms. Bridget",female,,0,0,364856,7.75,,Q
+981,2,"Wells, Master. Ralph Lester",male,2,1,1,29103,23,,S
+982,3,"Dyker, Mrs. Adolf Fredrik (Anna Elisabeth Judith Andersson)",female,22,1,0,347072,13.9,,S
+983,3,"Pedersen, Mr. Olaf",male,,0,0,345498,7.775,,S
+984,1,"Davidson, Mrs. Thornton (Orian Hays)",female,27,1,2,F.C. 12750,52,B71,S
+985,3,"Guest, Mr. Robert",male,,0,0,376563,8.05,,S
+986,1,"Birnbaum, Mr. Jakob",male,25,0,0,13905,26,,C
+987,3,"Tenglin, Mr. Gunnar Isidor",male,25,0,0,350033,7.7958,,S
+988,1,"Cavendish, Mrs. Tyrell William (Julia Florence Siegel)",female,76,1,0,19877,78.85,C46,S
+989,3,"Makinen, Mr. Kalle Edvard",male,29,0,0,STON/O 2. 3101268,7.925,,S
+990,3,"Braf, Miss. Elin Ester Maria",female,20,0,0,347471,7.8542,,S
+991,3,"Nancarrow, Mr. William Henry",male,33,0,0,A./5. 3338,8.05,,S
+992,1,"Stengel, Mrs. Charles Emil Henry (Annie May Morris)",female,43,1,0,11778,55.4417,C116,C
+993,2,"Weisz, Mr. Leopold",male,27,1,0,228414,26,,S
+994,3,"Foley, Mr. William",male,,0,0,365235,7.75,,Q
+995,3,"Johansson Palmquist, Mr. Oskar Leander",male,26,0,0,347070,7.775,,S
+996,3,"Thomas, Mrs. Alexander (Thamine Thelma"")""",female,16,1,1,2625,8.5167,,C
+997,3,"Holthen, Mr. Johan Martin",male,28,0,0,C 4001,22.525,,S
+998,3,"Buckley, Mr. Daniel",male,21,0,0,330920,7.8208,,Q
+999,3,"Ryan, Mr. Edward",male,,0,0,383162,7.75,,Q
+1000,3,"Willer, Mr. Aaron (Abi Weller"")""",male,,0,0,3410,8.7125,,S
+1001,2,"Swane, Mr. George",male,18.5,0,0,248734,13,F,S
+1002,2,"Stanton, Mr. Samuel Ward",male,41,0,0,237734,15.0458,,C
+1003,3,"Shine, Miss. Ellen Natalia",female,,0,0,330968,7.7792,,Q
+1004,1,"Evans, Miss. Edith Corse",female,36,0,0,PC 17531,31.6792,A29,C
+1005,3,"Buckley, Miss. Katherine",female,18.5,0,0,329944,7.2833,,Q
+1006,1,"Straus, Mrs. Isidor (Rosalie Ida Blun)",female,63,1,0,PC 17483,221.7792,C55 C57,S
+1007,3,"Chronopoulos, Mr. Demetrios",male,18,1,0,2680,14.4542,,C
+1008,3,"Thomas, Mr. John",male,,0,0,2681,6.4375,,C
+1009,3,"Sandstrom, Miss. Beatrice Irene",female,1,1,1,PP 9549,16.7,G6,S
+1010,1,"Beattie, Mr. Thomson",male,36,0,0,13050,75.2417,C6,C
+1011,2,"Chapman, Mrs. John Henry (Sara Elizabeth Lawry)",female,29,1,0,SC/AH 29037,26,,S
+1012,2,"Watt, Miss. Bertha J",female,12,0,0,C.A. 33595,15.75,,S
+1013,3,"Kiernan, Mr. John",male,,1,0,367227,7.75,,Q
+1014,1,"Schabert, Mrs. Paul (Emma Mock)",female,35,1,0,13236,57.75,C28,C
+1015,3,"Carver, Mr. Alfred John",male,28,0,0,392095,7.25,,S
+1016,3,"Kennedy, Mr. John",male,,0,0,368783,7.75,,Q
+1017,3,"Cribb, Miss. Laura Alice",female,17,0,1,371362,16.1,,S
+1018,3,"Brobeck, Mr. Karl Rudolf",male,22,0,0,350045,7.7958,,S
+1019,3,"McCoy, Miss. Alicia",female,,2,0,367226,23.25,,Q
+1020,2,"Bowenur, Mr. Solomon",male,42,0,0,211535,13,,S
+1021,3,"Petersen, Mr. Marius",male,24,0,0,342441,8.05,,S
+1022,3,"Spinner, Mr. Henry John",male,32,0,0,STON/OQ. 369943,8.05,,S
+1023,1,"Gracie, Col. Archibald IV",male,53,0,0,113780,28.5,C51,C
+1024,3,"Lefebre, Mrs. Frank (Frances)",female,,0,4,4133,25.4667,,S
+1025,3,"Thomas, Mr. Charles P",male,,1,0,2621,6.4375,,C
+1026,3,"Dintcheff, Mr. Valtcho",male,43,0,0,349226,7.8958,,S
+1027,3,"Carlsson, Mr. Carl Robert",male,24,0,0,350409,7.8542,,S
+1028,3,"Zakarian, Mr. Mapriededer",male,26.5,0,0,2656,7.225,,C
+1029,2,"Schmidt, Mr. August",male,26,0,0,248659,13,,S
+1030,3,"Drapkin, Miss. Jennie",female,23,0,0,SOTON/OQ 392083,8.05,,S
+1031,3,"Goodwin, Mr. Charles Frederick",male,40,1,6,CA 2144,46.9,,S
+1032,3,"Goodwin, Miss. Jessie Allis",female,10,5,2,CA 2144,46.9,,S
+1033,1,"Daniels, Miss. Sarah",female,33,0,0,113781,151.55,,S
+1034,1,"Ryerson, Mr. Arthur Larned",male,61,1,3,PC 17608,262.375,B57 B59 B63 B66,C
+1035,2,"Beauchamp, Mr. Henry James",male,28,0,0,244358,26,,S
+1036,1,"Lindeberg-Lind, Mr. Erik Gustaf (Mr Edward Lingrey"")""",male,42,0,0,17475,26.55,,S
+1037,3,"Vander Planke, Mr. Julius",male,31,3,0,345763,18,,S
+1038,1,"Hilliard, Mr. Herbert Henry",male,,0,0,17463,51.8625,E46,S
+1039,3,"Davies, Mr. Evan",male,22,0,0,SC/A4 23568,8.05,,S
+1040,1,"Crafton, Mr. John Bertram",male,,0,0,113791,26.55,,S
+1041,2,"Lahtinen, Rev. William",male,30,1,1,250651,26,,S
+1042,1,"Earnshaw, Mrs. Boulton (Olive Potter)",female,23,0,1,11767,83.1583,C54,C
+1043,3,"Matinoff, Mr. Nicola",male,,0,0,349255,7.8958,,C
+1044,3,"Storey, Mr. Thomas",male,60.5,0,0,3701,,,S
+1045,3,"Klasen, Mrs. (Hulda Kristina Eugenia Lofqvist)",female,36,0,2,350405,12.1833,,S
+1046,3,"Asplund, Master. Filip Oscar",male,13,4,2,347077,31.3875,,S
+1047,3,"Duquemin, Mr. Joseph",male,24,0,0,S.O./P.P. 752,7.55,,S
+1048,1,"Bird, Miss. Ellen",female,29,0,0,PC 17483,221.7792,C97,S
+1049,3,"Lundin, Miss. Olga Elida",female,23,0,0,347469,7.8542,,S
+1050,1,"Borebank, Mr. John James",male,42,0,0,110489,26.55,D22,S
+1051,3,"Peacock, Mrs. Benjamin (Edith Nile)",female,26,0,2,SOTON/O.Q. 3101315,13.775,,S
+1052,3,"Smyth, Miss. Julia",female,,0,0,335432,7.7333,,Q
+1053,3,"Touma, Master. Georges Youssef",male,7,1,1,2650,15.2458,,C
+1054,2,"Wright, Miss. Marion",female,26,0,0,220844,13.5,,S
+1055,3,"Pearce, Mr. Ernest",male,,0,0,343271,7,,S
+1056,2,"Peruschitz, Rev. Joseph Maria",male,41,0,0,237393,13,,S
+1057,3,"Kink-Heilmann, Mrs. Anton (Luise Heilmann)",female,26,1,1,315153,22.025,,S
+1058,1,"Brandeis, Mr. Emil",male,48,0,0,PC 17591,50.4958,B10,C
+1059,3,"Ford, Mr. Edward Watson",male,18,2,2,W./C. 6608,34.375,,S
+1060,1,"Cassebeer, Mrs. Henry Arthur Jr (Eleanor Genevieve Fosdick)",female,,0,0,17770,27.7208,,C
+1061,3,"Hellstrom, Miss. Hilda Maria",female,22,0,0,7548,8.9625,,S
+1062,3,"Lithman, Mr. Simon",male,,0,0,S.O./P.P. 251,7.55,,S
+1063,3,"Zakarian, Mr. Ortin",male,27,0,0,2670,7.225,,C
+1064,3,"Dyker, Mr. Adolf Fredrik",male,23,1,0,347072,13.9,,S
+1065,3,"Torfa, Mr. Assad",male,,0,0,2673,7.2292,,C
+1066,3,"Asplund, Mr. Carl Oscar Vilhelm Gustafsson",male,40,1,5,347077,31.3875,,S
+1067,2,"Brown, Miss. Edith Eileen",female,15,0,2,29750,39,,S
+1068,2,"Sincock, Miss. Maude",female,20,0,0,C.A. 33112,36.75,,S
+1069,1,"Stengel, Mr. Charles Emil Henry",male,54,1,0,11778,55.4417,C116,C
+1070,2,"Becker, Mrs. Allen Oliver (Nellie E Baumgardner)",female,36,0,3,230136,39,F4,S
+1071,1,"Compton, Mrs. Alexander Taylor (Mary Eliza Ingersoll)",female,64,0,2,PC 17756,83.1583,E45,C
+1072,2,"McCrie, Mr. James Matthew",male,30,0,0,233478,13,,S
+1073,1,"Compton, Mr. Alexander Taylor Jr",male,37,1,1,PC 17756,83.1583,E52,C
+1074,1,"Marvin, Mrs. Daniel Warner (Mary Graham Carmichael Farquarson)",female,18,1,0,113773,53.1,D30,S
+1075,3,"Lane, Mr. Patrick",male,,0,0,7935,7.75,,Q
+1076,1,"Douglas, Mrs. Frederick Charles (Mary Helene Baxter)",female,27,1,1,PC 17558,247.5208,B58 B60,C
+1077,2,"Maybery, Mr. Frank Hubert",male,40,0,0,239059,16,,S
+1078,2,"Phillips, Miss. Alice Frances Louisa",female,21,0,1,S.O./P.P. 2,21,,S
+1079,3,"Davies, Mr. Joseph",male,17,2,0,A/4 48873,8.05,,S
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datasets/train.csv

@@ -0,0 +1,892 @@
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+879,0,3,"Laleff, Mr. Kristo",male,,0,0,349217,7.8958,,S
+880,1,1,"Potter, Mrs. Thomas Jr (Lily Alexenia Wilson)",female,56,0,1,11767,83.1583,C50,C
+881,1,2,"Shelley, Mrs. William (Imanita Parrish Hall)",female,25,0,1,230433,26,,S
+882,0,3,"Markun, Mr. Johann",male,33,0,0,349257,7.8958,,S
+883,0,3,"Dahlberg, Miss. Gerda Ulrika",female,22,0,0,7552,10.5167,,S
+884,0,2,"Banfield, Mr. Frederick James",male,28,0,0,C.A./SOTON 34068,10.5,,S
+885,0,3,"Sutehall, Mr. Henry Jr",male,25,0,0,SOTON/OQ 392076,7.05,,S
+886,0,3,"Rice, Mrs. William (Margaret Norton)",female,39,0,5,382652,29.125,,Q
+887,0,2,"Montvila, Rev. Juozas",male,27,0,0,211536,13,,S
+888,1,1,"Graham, Miss. Margaret Edith",female,19,0,0,112053,30,B42,S
+889,0,3,"Johnston, Miss. Catherine Helen ""Carrie""",female,,1,2,W./C. 6607,23.45,,S
+890,1,1,"Behr, Mr. Karl Howell",male,26,0,0,111369,30,C148,C
+891,0,3,"Dooley, Mr. Patrick",male,32,0,0,370376,7.75,,Q

notebook.ipynb

@@ -0,0 +1,572 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Libraries\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import seaborn as sns\n",
+    "import matplotlib.pyplot as plt\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "from sklearn.ensemble import RandomForestClassifier\n",
+    "from sklearn.metrics import accuracy_score, classification_report, confusion_matrix, precision_score, recall_score, f1_score"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Load\n",
+    "train_df = pd.read_csv(\"datasets/train.csv\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Preprocess\n",
+    "train_df[\"Age\"] = train_df[\"Age\"].fillna(train_df[\"Age\"].median())\n",
+    "\n",
+    "train_df[\"Embarked\"] = train_df[\"Embarked\"].fillna(train_df[\"Embarked\"].mode()[0])\n",
+    "\n",
+    "train_df[\"Sex\"] = train_df[\"Sex\"].map({\"male\": 0, \"female\": 1})\n",
+    "\n",
+    "train_df = pd.get_dummies(train_df, columns=[\"Embarked\"], drop_first=True)\n",
+    "\n",
+    "train_df[\"FamilySize\"] = train_df[\"SibSp\"] + train_df[\"Parch\"] + 1\n",
+    "\n",
+    "train_df.drop([\"Name\", \"Ticket\", \"Cabin\", \"PassengerId\"], axis=1, inplace=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Split\n",
+    "X = train_df.drop(\"Survived\", axis=1)\n",
+    "y = train_df[\"Survived\"]\n",
+    "X_train, X_val, y_train, y_val = train_test_split(X, y, test_size=0.2, random_state=1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style>#sk-container-id-4 {\n",
+       "  /* Definition of color scheme common for light and dark mode */\n",
+       "  --sklearn-color-text: #000;\n",
+       "  --sklearn-color-text-muted: #666;\n",
+       "  --sklearn-color-line: gray;\n",
+       "  /* Definition of color scheme for unfitted estimators */\n",
+       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
+       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
+       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
+       "  --sklearn-color-unfitted-level-3: chocolate;\n",
+       "  /* Definition of color scheme for fitted estimators */\n",
+       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
+       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
+       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
+       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
+       "\n",
+       "  /* Specific color for light theme */\n",
+       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
+       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-icon: #696969;\n",
+       "\n",
+       "  @media (prefers-color-scheme: dark) {\n",
+       "    /* Redefinition of color scheme for dark theme */\n",
+       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
+       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-icon: #878787;\n",
+       "  }\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 pre {\n",
+       "  padding: 0;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 input.sk-hidden--visually {\n",
+       "  border: 0;\n",
+       "  clip: rect(1px 1px 1px 1px);\n",
+       "  clip: rect(1px, 1px, 1px, 1px);\n",
+       "  height: 1px;\n",
+       "  margin: -1px;\n",
+       "  overflow: hidden;\n",
+       "  padding: 0;\n",
+       "  position: absolute;\n",
+       "  width: 1px;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-dashed-wrapped {\n",
+       "  border: 1px dashed var(--sklearn-color-line);\n",
+       "  margin: 0 0.4em 0.5em 0.4em;\n",
+       "  box-sizing: border-box;\n",
+       "  padding-bottom: 0.4em;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-container {\n",
+       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
+       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
+       "     so we also need the `!important` here to be able to override the\n",
+       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
+       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
+       "  display: inline-block !important;\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-text-repr-fallback {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-parallel-item,\n",
+       "div.sk-serial,\n",
+       "div.sk-item {\n",
+       "  /* draw centered vertical line to link estimators */\n",
+       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
+       "  background-size: 2px 100%;\n",
+       "  background-repeat: no-repeat;\n",
+       "  background-position: center center;\n",
+       "}\n",
+       "\n",
+       "/* Parallel-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-4 div.sk-parallel-item::after {\n",
+       "  content: \"\";\n",
+       "  width: 100%;\n",
+       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
+       "  flex-grow: 1;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-parallel {\n",
+       "  display: flex;\n",
+       "  align-items: stretch;\n",
+       "  justify-content: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-parallel-item {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-parallel-item:first-child::after {\n",
+       "  align-self: flex-end;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-parallel-item:last-child::after {\n",
+       "  align-self: flex-start;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-parallel-item:only-child::after {\n",
+       "  width: 0;\n",
+       "}\n",
+       "\n",
+       "/* Serial-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-4 div.sk-serial {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "  align-items: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  padding-right: 1em;\n",
+       "  padding-left: 1em;\n",
+       "}\n",
+       "\n",
+       "\n",
+       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
+       "clickable and can be expanded/collapsed.\n",
+       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
+       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
+       "*/\n",
+       "\n",
+       "/* Pipeline and ColumnTransformer style (default) */\n",
+       "\n",
+       "#sk-container-id-4 div.sk-toggleable {\n",
+       "  /* Default theme specific background. It is overwritten whether we have a\n",
+       "  specific estimator or a Pipeline/ColumnTransformer */\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable label */\n",
+       "#sk-container-id-4 label.sk-toggleable__label {\n",
+       "  cursor: pointer;\n",
+       "  display: flex;\n",
+       "  width: 100%;\n",
+       "  margin-bottom: 0;\n",
+       "  padding: 0.5em;\n",
+       "  box-sizing: border-box;\n",
+       "  text-align: center;\n",
+       "  align-items: start;\n",
+       "  justify-content: space-between;\n",
+       "  gap: 0.5em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 label.sk-toggleable__label .caption {\n",
+       "  font-size: 0.6rem;\n",
+       "  font-weight: lighter;\n",
+       "  color: var(--sklearn-color-text-muted);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 label.sk-toggleable__label-arrow:before {\n",
+       "  /* Arrow on the left of the label */\n",
+       "  content: \"▸\";\n",
+       "  float: left;\n",
+       "  margin-right: 0.25em;\n",
+       "  color: var(--sklearn-color-icon);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 label.sk-toggleable__label-arrow:hover:before {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable content - dropdown */\n",
+       "\n",
+       "#sk-container-id-4 div.sk-toggleable__content {\n",
+       "  max-height: 0;\n",
+       "  max-width: 0;\n",
+       "  overflow: hidden;\n",
+       "  text-align: left;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-toggleable__content.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-toggleable__content pre {\n",
+       "  margin: 0.2em;\n",
+       "  border-radius: 0.25em;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-toggleable__content.fitted pre {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+       "  /* Expand drop-down */\n",
+       "  max-height: 200px;\n",
+       "  max-width: 100%;\n",
+       "  overflow: auto;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+       "  content: \"▾\";\n",
+       "}\n",
+       "\n",
+       "/* Pipeline/ColumnTransformer-specific style */\n",
+       "\n",
+       "#sk-container-id-4 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific style */\n",
+       "\n",
+       "/* Colorize estimator box */\n",
+       "#sk-container-id-4 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-label label.sk-toggleable__label,\n",
+       "#sk-container-id-4 div.sk-label label {\n",
+       "  /* The background is the default theme color */\n",
+       "  color: var(--sklearn-color-text-on-default-background);\n",
+       "}\n",
+       "\n",
+       "/* On hover, darken the color of the background */\n",
+       "#sk-container-id-4 div.sk-label:hover label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Label box, darken color on hover, fitted */\n",
+       "#sk-container-id-4 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator label */\n",
+       "\n",
+       "#sk-container-id-4 div.sk-label label {\n",
+       "  font-family: monospace;\n",
+       "  font-weight: bold;\n",
+       "  display: inline-block;\n",
+       "  line-height: 1.2em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-label-container {\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific */\n",
+       "#sk-container-id-4 div.sk-estimator {\n",
+       "  font-family: monospace;\n",
+       "  border: 1px dotted var(--sklearn-color-border-box);\n",
+       "  border-radius: 0.25em;\n",
+       "  box-sizing: border-box;\n",
+       "  margin-bottom: 0.5em;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-estimator.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "/* on hover */\n",
+       "#sk-container-id-4 div.sk-estimator:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-estimator.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
+       "\n",
+       "/* Common style for \"i\" and \"?\" */\n",
+       "\n",
+       ".sk-estimator-doc-link,\n",
+       "a:link.sk-estimator-doc-link,\n",
+       "a:visited.sk-estimator-doc-link {\n",
+       "  float: right;\n",
+       "  font-size: smaller;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1em;\n",
+       "  height: 1em;\n",
+       "  width: 1em;\n",
+       "  text-decoration: none !important;\n",
+       "  margin-left: 0.5em;\n",
+       "  text-align: center;\n",
+       "  /* unfitted */\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted,\n",
+       "a:link.sk-estimator-doc-link.fitted,\n",
+       "a:visited.sk-estimator-doc-link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "/* Span, style for the box shown on hovering the info icon */\n",
+       ".sk-estimator-doc-link span {\n",
+       "  display: none;\n",
+       "  z-index: 9999;\n",
+       "  position: relative;\n",
+       "  font-weight: normal;\n",
+       "  right: .2ex;\n",
+       "  padding: .5ex;\n",
+       "  margin: .5ex;\n",
+       "  width: min-content;\n",
+       "  min-width: 20ex;\n",
+       "  max-width: 50ex;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  box-shadow: 2pt 2pt 4pt #999;\n",
+       "  /* unfitted */\n",
+       "  background: var(--sklearn-color-unfitted-level-0);\n",
+       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted span {\n",
+       "  /* fitted */\n",
+       "  background: var(--sklearn-color-fitted-level-0);\n",
+       "  border: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link:hover span {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
+       "\n",
+       "#sk-container-id-4 a.estimator_doc_link {\n",
+       "  float: right;\n",
+       "  font-size: 1rem;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1rem;\n",
+       "  height: 1rem;\n",
+       "  width: 1rem;\n",
+       "  text-decoration: none;\n",
+       "  /* unfitted */\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 a.estimator_doc_link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "#sk-container-id-4 a.estimator_doc_link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 a.estimator_doc_link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "</style><div id=\"sk-container-id-4\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestClassifier(random_state=1)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" checked><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>RandomForestClassifier</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.6/modules/generated/sklearn.ensemble.RandomForestClassifier.html\">?<span>Documentation for RandomForestClassifier</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\"><pre>RandomForestClassifier(random_state=1)</pre></div> </div></div></div></div>"
+      ],
+      "text/plain": [
+       "RandomForestClassifier(random_state=1)"
+      ]
+     },
+     "execution_count": 36,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Train\n",
+    "model = RandomForestClassifier(n_estimators=100, random_state=1)\n",
+    "model.fit(X_train, y_train)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Accuracy: 0.7821229050279329\n",
+      "Precision: 0.7931034482758621\n",
+      "Recall: 0.6301369863013698\n",
+      "F1 Score: 0.7022900763358778\n",
+      "Classification Report:\n",
+      "               precision    recall  f1-score   support\n",
+      "\n",
+      "           0       0.78      0.89      0.83       106\n",
+      "           1       0.79      0.63      0.70        73\n",
+      "\n",
+      "    accuracy                           0.78       179\n",
+      "   macro avg       0.78      0.76      0.77       179\n",
+      "weighted avg       0.78      0.78      0.78       179\n",
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Evaluation\n",
+    "y_pred = model.predict(X_val)\n",
+    "y_pred = model.predict(X_val)\n",
+    "print(\"Accuracy:\", accuracy_score(y_val, y_pred))\n",
+    "print(\"Precision:\", precision_score(y_val, y_pred))\n",
+    "print(\"Recall:\", recall_score(y_val, y_pred))\n",
+    "print(\"F1 Score:\", f1_score(y_val, y_pred))\n",
+    "print(\"Classification Report:\\n\", classification_report(y_val, y_pred))\n",
+    "sns.heatmap(confusion_matrix(y_val, y_pred), annot=True, fmt=\"d\", cmap=\"Blues\")\n",
+    "plt.show()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "venv",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

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