Mercedes-Benz Greener Manufacturing.ipynb

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   "source": [
    "# Create an ML algorithm that can accurately predict the time a car will spend on the test bench \n",
    "# based on the vehicle configuration\n",
    "# Credentials - kasham1991@gmail.com / karan sharma\n",
    "\n",
    "\n",
    "# Agenda\n",
    "# 1. If for any column(s), the variance is equal to zero, then you need to remove those variable(s)\n",
    "# 2. Check for null and unique values for test and train sets\n",
    "# 3. Apply label encoder for categorical variables\n",
    "# 4. Perform dimensaionlity reduction with PCA\n",
    "# 5. Predict the test_df values using xgboost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
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   "source": [
    "# Importing the required libraries\n",
    "# Loading the train/test data\n",
    "# The lowercase alphabets are categorical variables\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "train = pd.read_csv('C://Datasets//MERCtrain.csv')\n",
    "train.head()\n",
    "# train.info()\n",
    "# print('Size of training set')\n",
    "# train.shape"
   ]
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  {
   "cell_type": "code",
   "execution_count": 3,
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     "execution_count": 3,
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   ],
   "source": [
    "# Separating y column as this is for pediction output\n",
    "y_train = train['y'].values\n",
    "y_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
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    {
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     "text": [
      "376\n",
      "int64     368\n",
      "object      8\n",
      "dtype: int64\n"
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    "# A lot of columns that have an X \n",
    "# Let's check for the same \n",
    "# 376 features with X\n",
    "colums_x = [c for c in train.columns if 'X' in c]\n",
    "# colums_x\n",
    "print(len(colums_x))\n",
    "print(train[colums_x].dtypes.value_counts())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
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   "source": [
    "# Looking at the test datset for simiilar features\n",
    "test = pd.read_csv('C://Datasets//MERCtest.csv')\n",
    "test.head()\n",
    "# train.info()\n",
    "# print('Size of training set')\n",
    "# train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Creating the final dataset\n",
    "# Removing unwanted columns (ID); y has been removed earlier\n",
    "final_column = list(set(train.columns) - set(['ID', 'y']))\n",
    "\n",
    "x_train = train[final_column]\n",
    "# x_train\n",
    "x_test = test[final_column]\n",
    "# x_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "No\n",
      "No\n"
     ]
    }
   ],
   "source": [
    "# Searching for null values\n",
    "# Creating a function for the same\n",
    "# There are no missin values\n",
    "def detect(df):\n",
    "    if df.isnull().any().any():\n",
    "        print(\"Yes\")\n",
    "    else:\n",
    "        print(\"No\")\n",
    "\n",
    "detect(x_train)\n",
    "detect(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
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    {
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      "<ipython-input-8-5c9054a05e2d>:11: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  x_train[column] = x_train[column].apply(mapit)\n",
      "<ipython-input-8-5c9054a05e2d>:12: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  x_test[column] = x_test[column].apply(mapit)\n"
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 376 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   X253  X348  X250  X142  X84  X146  X338  X96  X373  X383  ...  X158  X332  \\\n",
       "0     0     0     0     1    0     0     0    0     0     0  ...     0     0   \n",
       "1     0     1     1     1    0     0     0    1     0     0  ...     0     0   \n",
       "2     0     1     1     0    1     0     0    1     0     0  ...     1     0   \n",
       "3     0     1     1     1    1     0     0    1     0     0  ...     0     0   \n",
       "4     0     1     1     0    0     0     0    1     0     0  ...     1     0   \n",
       "\n",
       "   X336  X164  X366  X186  X178  X362  X23  X75  \n",
       "0     0     0     0     0     0     0    0    0  \n",
       "1     1     0     0     0     1     0    0    0  \n",
       "2     0     0     0     0     0     0    0    1  \n",
       "3     0     0     0     0     0     0    0    0  \n",
       "4     0     0     0     0     0     0    0    0  \n",
       "\n",
       "[5 rows x 376 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Removal of columns with a variance of 0\n",
    "# Column with a variance of 1 is irrelevant so we drop it\n",
    "\n",
    "for column in final_column:\n",
    "    check = len(np.unique(x_train[column]))\n",
    "    if check == 1:\n",
    "        x_train.drop(column, axis = 1) \n",
    "        x_test.drop(column, axis = 1)\n",
    "    if check > 2: # Column is categorical; hence mapping to ordinal measure of value\n",
    "        mapit = lambda x: sum([ord(digit) for digit in x])\n",
    "        x_train[column] = x_train[column].apply(mapit)\n",
    "        x_test[column] = x_test[column].apply(mapit)\n",
    "\n",
    "x_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Performing dimensionality reduction with principal components analysis\n",
    "from sklearn.decomposition import PCA\n",
    "n_comp = 12\n",
    "pca = PCA(n_components = n_comp, random_state = 42)\n",
    "pca_result_train = pca.fit_transform(x_train)\n",
    "pca_result_test = pca.transform(x_test)\n",
    "# print(pca_result_train)\n",
    "# print(pca_result_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# ML Modeling with XGboost\n",
    "import xgboost as xgb\n",
    "from sklearn.metrics import r2_score\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "# Splitting the data by 80/20\n",
    "x_train, x_valid, y_train, y_valid = train_test_split(pca_result_train, y_train, test_size = 0.2, random_state = 42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Building the final feature set\n",
    "f_train = xgb.DMatrix(x_train, label = y_train)\n",
    "f_valid = xgb.DMatrix(x_valid, label = y_valid)\n",
    "f_test = xgb.DMatrix(x_test)\n",
    "f_test = xgb.DMatrix(pca_result_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Setting the parameters for XGB\n",
    "params = {}\n",
    "params['objective'] = 'reg:linear'\n",
    "params['eta'] = 0.02\n",
    "params['max_depth'] = 4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[23:07:25] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.2.0/src/objective/regression_obj.cu:174: reg:linear is now deprecated in favor of reg:squarederror.\n",
      "[0]\ttrain-rmse:98.99695\tvalid-rmse:98.88884\ttrain-r2:-59.49733\tvalid-r2:-61.82690\n",
      "Multiple eval metrics have been passed: 'valid-r2' will be used for early stopping.\n",
      "\n",
      "Will train until valid-r2 hasn't improved in 50 rounds.\n",
      "[10]\ttrain-rmse:81.14410\tvalid-rmse:81.07848\ttrain-r2:-39.64492\tvalid-r2:-41.23399\n",
      "[20]\ttrain-rmse:66.59753\tvalid-rmse:66.55611\ttrain-r2:-26.37844\tvalid-r2:-27.45948\n",
      "[30]\ttrain-rmse:54.75785\tvalid-rmse:54.73342\ttrain-r2:-17.50910\tvalid-r2:-18.24670\n",
      "[40]\ttrain-rmse:45.14008\tvalid-rmse:45.13794\ttrain-r2:-11.57816\tvalid-r2:-12.08984\n",
      "[50]\ttrain-rmse:37.34669\tvalid-rmse:37.35502\ttrain-r2:-7.60987\tvalid-r2:-7.96497\n",
      "[60]\ttrain-rmse:31.04961\tvalid-rmse:31.08432\ttrain-r2:-4.95120\tvalid-r2:-5.20775\n",
      "[70]\ttrain-rmse:25.98429\tvalid-rmse:26.03124\ttrain-r2:-3.16787\tvalid-r2:-3.35352\n",
      "[80]\ttrain-rmse:21.93689\tvalid-rmse:21.99638\ttrain-r2:-1.97059\tvalid-r2:-2.10852\n",
      "[90]\ttrain-rmse:18.73373\tvalid-rmse:18.81611\ttrain-r2:-1.16641\tvalid-r2:-1.27463\n",
      "[100]\ttrain-rmse:16.22580\tvalid-rmse:16.33562\ttrain-r2:-0.62519\tvalid-r2:-0.71444\n",
      "[110]\ttrain-rmse:14.28492\tvalid-rmse:14.42676\ttrain-r2:-0.25965\tvalid-r2:-0.33718\n",
      "[120]\ttrain-rmse:12.80854\tvalid-rmse:12.97929\ttrain-r2:-0.01273\tvalid-r2:-0.08231\n",
      "[130]\ttrain-rmse:11.68853\tvalid-rmse:11.89459\ttrain-r2:0.15664\tvalid-r2:0.09103\n",
      "[140]\ttrain-rmse:10.85621\tvalid-rmse:11.11407\ttrain-r2:0.27247\tvalid-r2:0.20641\n",
      "[150]\ttrain-rmse:10.24134\tvalid-rmse:10.54714\ttrain-r2:0.35255\tvalid-r2:0.28530\n",
      "[160]\ttrain-rmse:9.78163\tvalid-rmse:10.14432\ttrain-r2:0.40937\tvalid-r2:0.33885\n",
      "[170]\ttrain-rmse:9.44156\tvalid-rmse:9.86208\ttrain-r2:0.44972\tvalid-r2:0.37513\n",
      "[180]\ttrain-rmse:9.18797\tvalid-rmse:9.66085\ttrain-r2:0.47889\tvalid-r2:0.40037\n",
      "[190]\ttrain-rmse:9.00239\tvalid-rmse:9.52693\ttrain-r2:0.49973\tvalid-r2:0.41688\n",
      "[200]\ttrain-rmse:8.86060\tvalid-rmse:9.43714\ttrain-r2:0.51536\tvalid-r2:0.42782\n",
      "[210]\ttrain-rmse:8.75668\tvalid-rmse:9.37549\ttrain-r2:0.52666\tvalid-r2:0.43527\n",
      "[220]\ttrain-rmse:8.67821\tvalid-rmse:9.33270\ttrain-r2:0.53511\tvalid-r2:0.44042\n",
      "[230]\ttrain-rmse:8.61165\tvalid-rmse:9.30380\ttrain-r2:0.54221\tvalid-r2:0.44388\n",
      "[240]\ttrain-rmse:8.55436\tvalid-rmse:9.27646\ttrain-r2:0.54828\tvalid-r2:0.44714\n",
      "[250]\ttrain-rmse:8.51487\tvalid-rmse:9.26083\ttrain-r2:0.55244\tvalid-r2:0.44900\n",
      "[260]\ttrain-rmse:8.47442\tvalid-rmse:9.24864\ttrain-r2:0.55668\tvalid-r2:0.45045\n",
      "[270]\ttrain-rmse:8.44006\tvalid-rmse:9.23549\ttrain-r2:0.56027\tvalid-r2:0.45201\n",
      "[280]\ttrain-rmse:8.40665\tvalid-rmse:9.22674\ttrain-r2:0.56375\tvalid-r2:0.45305\n",
      "[290]\ttrain-rmse:8.37692\tvalid-rmse:9.21810\ttrain-r2:0.56683\tvalid-r2:0.45407\n",
      "[300]\ttrain-rmse:8.33855\tvalid-rmse:9.21329\ttrain-r2:0.57079\tvalid-r2:0.45464\n",
      "[310]\ttrain-rmse:8.31107\tvalid-rmse:9.20749\ttrain-r2:0.57361\tvalid-r2:0.45533\n",
      "[320]\ttrain-rmse:8.27727\tvalid-rmse:9.20469\ttrain-r2:0.57707\tvalid-r2:0.45566\n",
      "[330]\ttrain-rmse:8.25049\tvalid-rmse:9.19987\ttrain-r2:0.57980\tvalid-r2:0.45623\n",
      "[340]\ttrain-rmse:8.22683\tvalid-rmse:9.19602\ttrain-r2:0.58221\tvalid-r2:0.45669\n",
      "[350]\ttrain-rmse:8.20103\tvalid-rmse:9.19544\ttrain-r2:0.58483\tvalid-r2:0.45676\n",
      "[360]\ttrain-rmse:8.17925\tvalid-rmse:9.19402\ttrain-r2:0.58703\tvalid-r2:0.45692\n",
      "[370]\ttrain-rmse:8.15078\tvalid-rmse:9.19535\ttrain-r2:0.58990\tvalid-r2:0.45676\n",
      "[380]\ttrain-rmse:8.12257\tvalid-rmse:9.19431\ttrain-r2:0.59273\tvalid-r2:0.45689\n",
      "[390]\ttrain-rmse:8.09906\tvalid-rmse:9.19362\ttrain-r2:0.59509\tvalid-r2:0.45697\n",
      "[400]\ttrain-rmse:8.07960\tvalid-rmse:9.19173\ttrain-r2:0.59703\tvalid-r2:0.45719\n",
      "[410]\ttrain-rmse:8.05309\tvalid-rmse:9.19057\ttrain-r2:0.59967\tvalid-r2:0.45733\n",
      "[420]\ttrain-rmse:8.02591\tvalid-rmse:9.19310\ttrain-r2:0.60237\tvalid-r2:0.45703\n",
      "[430]\ttrain-rmse:8.00222\tvalid-rmse:9.19282\ttrain-r2:0.60471\tvalid-r2:0.45706\n",
      "[440]\ttrain-rmse:7.97309\tvalid-rmse:9.19328\ttrain-r2:0.60759\tvalid-r2:0.45701\n",
      "[450]\ttrain-rmse:7.95003\tvalid-rmse:9.19220\ttrain-r2:0.60985\tvalid-r2:0.45714\n",
      "Stopping. Best iteration:\n",
      "[407]\ttrain-rmse:8.05899\tvalid-rmse:9.18951\ttrain-r2:0.59908\tvalid-r2:0.45746\n",
      "\n",
      "[23:07:29] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.2.0/src/objective/regression_obj.cu:174: reg:linear is now deprecated in favor of reg:squarederror.\n"
     ]
    }
   ],
   "source": [
    "# Predicting the score\n",
    "# Creating a function for the same\n",
    "\n",
    "def scorer(m, w):\n",
    "    labels = w.get_label()\n",
    "    return 'r2', r2_score(labels, m)\n",
    "\n",
    "final_set = [(f_train, 'train'), (f_valid, 'valid')]\n",
    "\n",
    "P = xgb.train(params, f_train, 1000, final_set, early_stopping_rounds=50, feval=scorer, maximize=True, verbose_eval=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 79.28774,  96.29256,  81.22566, ...,  98.70664, 107.1244 ,\n",
       "        95.05699], dtype=float32)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Predicting on test set\n",
    "p_test = P.predict(f_test)\n",
    "p_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>79.287743</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>96.292557</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>81.225662</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>77.318939</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>109.973053</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            y\n",
       "0   79.287743\n",
       "1   96.292557\n",
       "2   81.225662\n",
       "3   77.318939\n",
       "4  109.973053"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Predicted_Data = pd.DataFrame()\n",
    "Predicted_Data['y'] = p_test\n",
    "Predicted_Data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Thank You :) "
   ]
  }
 ],
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