hollbacdario/cds-1011-health-data-machine-learning
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adding elevation

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Dario Hollbach 2025-11-19 22:13:59 +01:00
parent e82e42cf0f
commit 53e9c8adc2
1 changed files with 7 additions and 2 deletions
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ml_calories.ipynb
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@ -41,6 +41,7 @@
"cols = {\n", "cols = {\n",
" \"Aktivitätstyp\": \"activity_type\",\n", " \"Aktivitätstyp\": \"activity_type\",\n",
" \"Distanz\": \"distance_km\",\n", " \"Distanz\": \"distance_km\",\n",
" \"Anstieg gesamt\": \"elevation_meters\",\n",
" \"Kalorien\": \"calories_burned\",\n", " \"Kalorien\": \"calories_burned\",\n",
" \"Zeit\": \"duration_str\",\n", " \"Zeit\": \"duration_str\",\n",
" \"Ø Herzfrequenz\": \"heart_rate\"\n", " \"Ø Herzfrequenz\": \"heart_rate\"\n",
@ -109,6 +110,8 @@
"# get rid of commas in Distance and Calories Burned columns and convert to numeric\n", "# get rid of commas in Distance and Calories Burned columns and convert to numeric\n",
"data['distance_km'] = pd.to_numeric(\n", "data['distance_km'] = pd.to_numeric(\n",
" data['distance_km'].str.replace(',', ''), errors='coerce')\n", " data['distance_km'].str.replace(',', ''), errors='coerce')\n",
"data['elevation_meters'] = pd.to_numeric(\n",
" data['elevation_meters'].str.replace(',', ''), errors='coerce')\n",
"data['calories_burned'] = pd.to_numeric(\n", "data['calories_burned'] = pd.to_numeric(\n",
" data['calories_burned'].str.replace(',', ''), errors='coerce')\n", " data['calories_burned'].str.replace(',', ''), errors='coerce')\n",
"data['heart_rate'] = pd.to_numeric(\n", "data['heart_rate'] = pd.to_numeric(\n",
@ -306,7 +309,8 @@
"outputs": [], "outputs": [],
"source": [ "source": [
"# define features and target variable\n", "# define features and target variable\n",
"features = ['activity_type', 'distance_km', 'duration_seconds', 'heart_rate']\n", "features = ['activity_type', 'distance_km',\n",
" 'elevation_meters', 'duration_seconds', 'heart_rate']\n",
"target = 'calories_burned'\n", "target = 'calories_burned'\n",
"\n", "\n",
"x = data[features]\n", "x = data[features]\n",
@ -319,7 +323,8 @@
" f\"Data split into {x_train.shape[0]} training rows and {x_test.shape[0]} testing rows.\")\n", " f\"Data split into {x_train.shape[0]} training rows and {x_test.shape[0]} testing rows.\")\n",
"\n", "\n",
"# Preprocessing: Scaling numeric features and encoding categorical features\n", "# Preprocessing: Scaling numeric features and encoding categorical features\n",
"numeric_features = ['distance_km', 'duration_seconds', 'heart_rate']\n", "numeric_features = ['distance_km', 'elevation_meters',\n",
" 'duration_seconds', 'heart_rate']\n",
"categorical_features = ['activity_type']\n", "categorical_features = ['activity_type']\n",
"preprocessor = ColumnTransformer(\n", "preprocessor = ColumnTransformer(\n",
" transformers=[\n", " transformers=[\n",