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{
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"name": "stdout",
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"text": [
"Age 0\n",
"Gender 0\n",
"How many meals do you have a day? (number of regular occasions in a day when a significant and reasonably filling amount of food is eaten) 0\n",
"What would best describe your diet: 0\n",
"Choose all that apply: [I skip meals] 0\n",
"Choose all that apply: [I experience feelings of hunger during the day] 0\n",
"Choose all that apply: [I consult a nutritionist/dietician] 0\n",
"Choose all that apply: [I cook my own meals] 0\n",
"What would you consider to be the main meal of YOUR day? 0\n",
"What does your diet mostly consist of and how is it prepared? 0\n",
"How many times a week do you order-in or go out to eat? 0\n",
"Are you allergic to any of the following? (Tick all that apply) 0\n",
"What is your weekly food intake frequency of the following food categories: [Sweet foods] 0\n",
"What is your weekly food intake frequency of the following food categories: [Salty foods] 0\n",
"What is your weekly food intake frequency of the following food categories: [Fresh fruit] 0\n",
"What is your weekly food intake frequency of the following food categories: [Fresh vegetables] 0\n",
"What is your weekly food intake frequency of the following food categories: [Oily, fried foods] 0\n",
"What is your weekly food intake frequency of the following food categories: [Meat] 0\n",
"What is your weekly food intake frequency of the following food categories: [Seafood ] 0\n",
"How frequently do you consume these beverages [Tea] 0\n",
"How frequently do you consume these beverages [Coffee] 0\n",
"How frequently do you consume these beverages [Aerated (Soft) Drinks] 0\n",
"How frequently do you consume these beverages [Fruit Juices (Fresh/Packaged)] 0\n",
"How frequently do you consume these beverages [Dairy Beverages (Milk, Milkshakes, Smoothies, Buttermilk, etc)] 0\n",
"How frequently do you consume these beverages [Alcoholic Beverages] 0\n",
"What is your water consumption like (in a day, 1 cup=250ml approx) 0\n",
"Disease Risk 0\n",
"dtype: int64\n"
]
}
],
"source": [
"import pandas as pd\n",
"from sklearn.model_selection import train_test_split\n",
"from sklearn.preprocessing import LabelEncoder, StandardScaler\n",
"\n",
"# Load the data\n",
"data = pd.read_csv('data.csv')\n",
"\n",
"# Check for missing values\n",
"print(data.isnull().sum())\n",
"\n",
"# Encode categorical variables\n",
"label_encoders = {}\n",
"for column in data.select_dtypes(include=['object']).columns:\n",
" le = LabelEncoder()\n",
" data[column] = le.fit_transform(data[column])\n",
" label_encoders[column] = le\n",
"\n",
"# Split the data into features and target\n",
"X = data.drop(columns=['Disease Risk'])\n",
"y = data['Disease Risk']\n",
"\n",
"# Standardize the features\n",
"scaler = StandardScaler()\n",
"X_scaled = scaler.fit_transform(X)\n",
"\n",
"# Split the data into training and testing sets\n",
"X_train, X_test, y_train, y_test = train_test_split(X_scaled, y, test_size=0.2, random_state=42)\n"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Accuracy: 0.3333333333333333\n",
" precision recall f1-score support\n",
"\n",
" 0 0.00 0.00 0.00 2\n",
" 1 0.00 0.00 0.00 2\n",
" 2 0.33 1.00 0.50 2\n",
"\n",
" accuracy 0.33 6\n",
" macro avg 0.11 0.33 0.17 6\n",
"weighted avg 0.11 0.33 0.17 6\n",
"\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\USER\\AppData\\Roaming\\Python\\Python312\\site-packages\\sklearn\\metrics\\_classification.py:1509: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
" _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n",
"C:\\Users\\USER\\AppData\\Roaming\\Python\\Python312\\site-packages\\sklearn\\metrics\\_classification.py:1509: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
" _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n",
"C:\\Users\\USER\\AppData\\Roaming\\Python\\Python312\\site-packages\\sklearn\\metrics\\_classification.py:1509: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
" _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n"
]
}
],
"source": [
"from sklearn.ensemble import RandomForestClassifier\n",
"from sklearn.metrics import accuracy_score, classification_report\n",
"\n",
"# Initialize the model\n",
"model = RandomForestClassifier(n_estimators=100, random_state=42)\n",
"\n",
"# Train the model\n",
"model.fit(X_train, y_train)\n",
"\n",
"# Make predictions\n",
"y_pred = model.predict(X_test)\n",
"\n",
"# Evaluate the model\n",
"accuracy = accuracy_score(y_test, y_pred)\n",
"print(f'Accuracy: {accuracy}')\n",
"print(classification_report(y_test, y_pred))\n"
]
}
],
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