3.1 Regresi - Simple Linear Regression.ipynb

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   "source": [
    "# Simple Linear Regression"
   ]
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    "- SLR merupakan jenis regresi yang terdiri dari satu variable independent (prediktor) yang akan memprediksi variable dependent (target)\n",
    "- rumus SLR ...    **Y=βo+β1X+ϵ**\n",
    "    - Y  = variable target\n",
    "    - X  = variable prediktor\n",
    "    - βo = konstanta, perpotongan garis regresi dengan sumbu Y (nilai estimasi jika x = 0)\n",
    "    - β1 = koefisien regresi (slope)\n",
    "    - ϵ  = kesalahan acak  "
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    "### Tahapan dalam melakukan teknik SLR secara manual\n",
    "\n",
    "1. mencari apakah ada hubungan antara variable x dan y > *cukup melihat saja dataset yang ada*\n",
    "2. menentukan variable x dan variable y > * variabel x adalah variable pengaruh dan variable y adalah variable yang dipengaruhi*\n",
    "3. membuat scatter plot dari variabel x dan y \n",
    "4. analisis scatter plot, apakah ada hubungan liner ? jika ya maka model regresinya adalah SLR\n",
    "5. menghitung a (intercept/koefisien model regresi) dan b (slope/ kemiringan gradien model) \n",
    "6. menghitung estimasi nilai y-hat = bo + bX, jika x dimasukan ke dalam persamaan regresi\n",
    "7. membuat garis y-hat = bo + b1.X pada sumbu X dan Y"
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   "source": [
    "### Tahapan dalam melakukan teknik SLR menggunakan Python"
   ]
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   "source": [
    "#### 1. Import Library"
   ]
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    "library merupakan kumpulan fungsi-fungsi yang digunakan untuk melakukan tugas tertentu. Melalui penggunaan library, penulisan code menjadi efektif karena terhindar penulisan formula yang detail, berulang dan rapih. Untuk itu kita harus menggunakan library sesuai tugas dan kegunaannya melalui pemanggilan library terlebih dahulu.  "
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   "source": [
    "import pandas as pd # umum digunakan untuk mengubah data, membaca data dll\n",
    "import numpy as np # melakukan operasi perhitungan numerik\n",
    "from sklearn import linear_model # memanggil model regresi\n",
    "from sklearn.linear_model import LinearRegression # memanggil regresi linear \n",
    "from sklearn.model_selection import train_test_split # memisahkan data train dan data test\n",
    "import seaborn as sns # menampilkan visualisasi data\n",
    "from sklearn import metrics # memanggil berbagai metrics evaluasi\n",
    "import matplotlib.pyplot as plt # melakukan visualisasi data\n",
    "%matplotlib inline "
   ]
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   "source": [
    "#### 2. Membaca Data "
   ]
  },
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   "source": [
    "Setelah library dipanggil kedalam notebook, maka dataset yang ada di dalam komputer dipindahkan ke dalam notebook melalui import file yang selanjuntya disimpan ke dalam suatu variable atau objek."
   ]
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Hours</th>\n",
       "      <th>Scores</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2.5</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5.1</td>\n",
       "      <td>47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3.2</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>8.5</td>\n",
       "      <td>75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3.5</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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       "   Hours  Scores\n",
       "0    2.5      21\n",
       "1    5.1      47\n",
       "2    3.2      27\n",
       "3    8.5      75\n",
       "4    3.5      30"
      ]
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   "source": [
    "df = pd.read_csv('dataset/nilai.csv') # memanggil data csv dan memasukan ke dalam variable df\n",
    "df.head() # cek 5 nilai teratas"
   ]
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   "source": [
    "Setelah data berhasil dibaca oleh notebook, maka tahap selanjutnya adalah data pre-processing. Tahapan ini bertujuan agar data yang digunakan dapat diterima oleh model."
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    "#### 3. Exploratory Data Analysis\n",
    "\n",
    "EDA merupakan serangkaian teknik dan metode yang digunakan untuk menggali dan memahami karakteristik utama dari dataset sebelum menjalankan analisis lebih lanjut atau membangun model. Tujuan EDA adalah untuk mengidentifikasi pola, anomali, dan hubungan dalam data secara deskriptif. Berikut hal yang dilakukan dalam EDA, "
   ]
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   "source": [
    "##### 3.1 Melihat dimensi dataset yang digunakan "
   ]
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       "(15, 2)"
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   "source": [
    "df.shape # melihat dimensi data "
   ]
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   "source": [
    "karena hanya ada dua variable maka dalam EDA yang paling sesuai adalah melakukan Bivariate Analysis"
   ]
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   "source": [
    "##### 3.2 Bivariate Analysis \n",
    "\n",
    "Bivariate analysis adalah Teknik analysis dan eksplorasi pada dua kolom dalam dataset. Umumnya menggunakan 1 kolom predictor dan 1 kolom target."
   ]
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   "source": [
    "##### 3.2.1 Melihat distribusi data "
   ]
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Hours</th>\n",
       "      <th>Scores</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>15.000000</td>\n",
       "      <td>15.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>4.833333</td>\n",
       "      <td>48.066667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2.623429</td>\n",
       "      <td>25.327756</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.100000</td>\n",
       "      <td>17.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2.950000</td>\n",
       "      <td>26.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>4.500000</td>\n",
       "      <td>42.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>6.800000</td>\n",
       "      <td>68.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>9.200000</td>\n",
       "      <td>88.000000</td>\n",
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       "           Hours     Scores\n",
       "count  15.000000  15.000000\n",
       "mean    4.833333  48.066667\n",
       "std     2.623429  25.327756\n",
       "min     1.100000  17.000000\n",
       "25%     2.950000  26.000000\n",
       "50%     4.500000  42.000000\n",
       "75%     6.800000  68.500000\n",
       "max     9.200000  88.000000"
      ]
     },
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   "source": [
    "df.describe() # menampilkan distribusi data "
   ]
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   "source": [
    "##### 3.2.2 Melakukan visualisasi Data "
   ]
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot(x = 'Hours', y = 'Scores', style = 'o')\n",
    "plt.xlabel('Scores')\n",
    "plt.ylabel('Hours')\n",
    "plt.title('Scores vs Hours')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "94893a3d-0bc0-4182-9dea-57d11fb79577",
   "metadata": {},
   "source": [
    "**insight:**\n",
    "- variable x dan variable y memiliki korelasi yang linear. Namun untuk lebih jelasnya gunakan garis gradien"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "5dae9371-2bc5-47be-acae-333758b36491",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.JointGrid at 0x25e9db6f6d0>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x600 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.jointplot(x=df['Hours'], y=df['Scores'], data=df, kind='reg')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "93e9bbce-0357-4aba-a986-9fd048e9acd2",
   "metadata": {
    "tags": []
   },
   "source": [
    "**insight:**\n",
    "- dari gambar diatas menunjukan korelasi linear yang semakin jelas\n",
    "- Hasil dari EDA diketahui bahwa dua variable saling berkorelasi linear sehingga memunkinkan untuk dilakukan pemodelan regresi linear "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "91b9c7c6-6c61-49d1-a78e-8ffbd7d3f830",
   "metadata": {
    "tags": []
   },
   "source": [
    "#### 4. Menentukan Variable\n",
    "\n",
    "Variable X dan variable Y ditentukan berdasarkan pengaruhnya. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "091b1d69-c43c-4c9f-b02e-940d8f65e371",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "X = df.iloc[:,:-1].values  # variable predictor\n",
    "y = df.iloc[:,1].values # variable target"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "abd388db-a689-48be-9d24-24bd22a81577",
   "metadata": {},
   "source": [
    "- `.iloc`    : Metode pengindeksan yang digunakan untuk memilih baris dan kolom berdasarkan indeks numerik.\n",
    "- `[:, :-1]` : Menggunakan slicing pada indeks kolom, ini mengambil semua baris (:) dan semua kolom kecuali yang terakhir (:-1). Dengan kata lain, ini mengambil semua fitur atau variabel independen kecuali variabel dependen yang terletak di kolom terakhir.\n",
    "- `[:,1]`    : Mengambil semua baris pada kolom pertama saja \n",
    "- `.values`  : Digunakan saat berurusan dengan algoritma atau fungsi yang mengharapkan input dalam bentuk array NumPy."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "9a5026e8-1f1d-4850-bfa2-4c15b6e6744b",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[2.5]\n",
      " [5.1]\n",
      " [3.2]\n",
      " [8.5]\n",
      " [3.5]\n",
      " [1.5]\n",
      " [9.2]\n",
      " [5.5]\n",
      " [8.3]\n",
      " [2.7]\n",
      " [7.7]\n",
      " [5.9]\n",
      " [4.5]\n",
      " [3.3]\n",
      " [1.1]]\n"
     ]
    }
   ],
   "source": [
    "print(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "c4b83c5d-6839-4573-8b62-4c688208833b",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[21 47 27 75 30 20 88 60 81 25 85 62 41 42 17]\n"
     ]
    }
   ],
   "source": [
    "print(y)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "db70b909-d381-4702-adbf-ab32b78046a8",
   "metadata": {},
   "source": [
    "#### 5. Splitting Data "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b965b04c-b1bf-45d9-a639-8e653c28bc0d",
   "metadata": {},
   "source": [
    "splitting data adalah proses memisahkan dataset menjadi dua yaitu data train dan data test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "eea316e3-12db-4649-af6d-fc2479e6fb1f",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train - Predictors shape (12, 1)\n",
      "Test - Predictors shape (3, 1)\n",
      "Train - Target shape (12, 1)\n",
      "Test - Target shape (3, 1)\n"
     ]
    }
   ],
   "source": [
    "x_train, x_test, y_train, y_test = train_test_split(df['Hours'], df['Scores'], test_size=0.2, random_state=0) # mengatur hyperparameter \n",
    "x_train = np.reshape(x_train, (-1,1))\n",
    "x_test = np.reshape(x_test, (-1,1))\n",
    "y_train = np.reshape(y_train, (-1,1))\n",
    "y_test = np.reshape(y_test, (-1,1))\n",
    "\n",
    "#\n",
    "print('Train - Predictors shape', x_train.shape)\n",
    "print('Test - Predictors shape', x_test.shape)\n",
    "print('Train - Target shape', y_train.shape)\n",
    "print('Test - Target shape', y_test.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8b534ecd-d492-4427-965d-c872764dcf6a",
   "metadata": {},
   "source": [
    "#### 6. Linear Regression using Scikit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "2a1743ef-e65b-4bb2-bfdc-f23bf5e7c7eb",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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      ],
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "regressor = LinearRegression() # algoritma regresi linear akan diwakili oleh variable regressor \n",
    "regressor.fit(x_train, y_train) # membuat model regresi linear berdasarkan dataset yang sudah disiapkan dan dipisahkan menjadi data training dan data testing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "2feed886-bd98-4ee2-9bf8-9ea465b756d1",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[2.32752006]\n"
     ]
    }
   ],
   "source": [
    "print(regressor.intercept_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "b4d35019-df79-4e82-b5a9-a7f5ef84da8b",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[9.56051622]]\n"
     ]
    }
   ],
   "source": [
    "print(regressor.coef_)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b0f29e5a-2dee-4737-a88e-b9a8f682ba2a",
   "metadata": {},
   "source": [
    "**insight:**\n",
    "- dari nilai koefisien dan intercept yang diperoleh diketahui bahwa\n",
    "- menggunakan rumus y = ax + b,... maka y = 9.56051622X + 2.32752006\n",
    "    - setiap X = 0 maka y = 2.32752006 artinya siswa yang tidak belajar sama sekali akan mendapatkan nilai sebesar 2.32752006\n",
    "    - setiap siswa menambahkan 1 jam belajar maka nilainya akan bertambah sebanyak 9.56051622"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b29f2ddc-2f1d-41be-a8e0-f33060911de4",
   "metadata": {
    "tags": []
   },
   "source": [
    "#### 7. Melakukan Prediksi Data "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "6d627bcf-55f7-4666-9a05-c2c3f6d689b1",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "y_pred = regressor.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "98ef9313-5a06-44cd-918f-c10d51b28583",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'copy_X': True, 'fit_intercept': True, 'n_jobs': None, 'positive': False}"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "regressor.get_params()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8b7532b2-7219-4298-a0dd-9b4980586e9f",
   "metadata": {},
   "source": [
    "- `get_params()` : Metode ini memberikan informasi tentang parameter-parameter yang dapat dikonfigurasi pada suatu model machine learning, membantu pengguna untuk memahami konfigurasi model dan melakukan penyesuaian jika diperlukan.\n",
    "- parameter seperti `fit_intercept`, `normalize`, dan lainnya yang dapat dikonfigurasi pada model regresi linear. Ketika kita memiliki pemahaman yang baik tentang parameter-parameter ini, kita dapat menggunakan metode .set_params() untuk mengubah nilai-nilai parameter atau menyesuaikan konfigurasi model sesuai kebutuhan spesifik."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "7687c38d-f42a-4ee0-9c2c-6c4b2ac56ec3",
   "metadata": {
    "tags": []
   },
   "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>Actual</th>\n",
       "      <th>Predicted</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>47</td>\n",
       "      <td>51.086153</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>88</td>\n",
       "      <td>90.284269</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>81</td>\n",
       "      <td>81.679805</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Actual  Predicted\n",
       "0      47  51.086153\n",
       "1      88  90.284269\n",
       "2      81  81.679805"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame({'Actual':y_test.ravel(), 'Predicted':y_pred.ravel()})\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8ef1025d-0245-4cdd-9aa0-fc54457b4eff",
   "metadata": {},
   "source": [
    "#### 8. Melakukan uji peforma model dengan metrics evaluasi"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1c33a0fa-b96a-45c4-a5d6-8b95550a2e17",
   "metadata": {},
   "source": [
    "##### 8.1 Mean Absolute Error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "7a84a0a7-efc9-40c4-97a0-b93c75d610e3",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Absolute Error 2.3500755726078424\n"
     ]
    }
   ],
   "source": [
    "print('Mean Absolute Error', metrics.mean_absolute_error(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dc9a1a4c-458b-4791-ad45-cbe57cc0644f",
   "metadata": {},
   "source": [
    "- MAE digunakan untuk menunjukan kesalahan rata-rata error nilai aktual(y_test) dan nilai prediksi (y_pred)\n",
    "- MAE adalah total keseluruhan rata-rata selisih dari nilai prediksi dengan nilai sebenarnya yang diabsolutkan, untuk menghindari nilai minus yang menyebabkan adanya bias\n",
    "- Semakin kecil nilai MAE maka semakin baik model regresi tersebut"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8763c861-65d8-422c-b72d-8591118daea8",
   "metadata": {},
   "source": [
    "##### 8.2 Mean Squared Error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "358ad465-a0d3-4d84-8987-20e8c5eee543",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Squared Error 7.458888326959026\n"
     ]
    }
   ],
   "source": [
    "print('Mean Squared Error', metrics.mean_squared_error(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "51b9f543-198a-4517-bd27-c645b55dbe60",
   "metadata": {
    "tags": []
   },
   "source": [
    "- MSE serupa dengan MAe, namun tidak diabsolutkan tetapi dikuadratkan\n",
    "- MSE digunakan untuk mengetahui adanya nilai yang tidak terduga (bukan outlier) \n",
    "- Dengan MSE, nilai yang diperoleh bisa lebih besar dua kali lipat dari nilai MAE yang diperoleh\n",
    "- Jika model regresi kita buruk maka metrics yang dihasilkan akan jauh lebih buruk karena dikuadratkan\n",
    "- karena menggunakan mean, metrics ini rentan terhadap outliers sehingga sulit untuuk digunakan untuk menentukan peforma model"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a20793b9-1370-4f27-a543-ee615d42aef4",
   "metadata": {},
   "source": [
    "##### MAE vs MSE\n",
    "\n",
    "Mana yang digunakan antara MAE atau MSE ?\n",
    "- tergantung pada data yang dimiliki\n",
    "- jika pada data terdapat outlier maka gunakan MAE\n",
    "- jika terdapat data yang tidak terduga dan perlu perhatian khusus gunakan MSE \n",
    "- MAE lebih banyak digunakan diberbagai kasus dibandingkan MSE "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6553e94a-0155-413c-80a5-f494f702ef0d",
   "metadata": {},
   "source": [
    "##### 8.3 Root Mean Squared Error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "7b9f43db-baab-4b1e-a25c-44d0b983305b",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Root Mean Squared Error 2.7310965429583454\n"
     ]
    }
   ],
   "source": [
    "print('Root Mean Squared Error', np.sqrt(metrics.mean_squared_error(y_test, y_pred)))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aae95dc4-351c-4e9e-90ca-c9556d427067",
   "metadata": {
    "tags": []
   },
   "source": [
    "- RMSE adalah nilai MSE yang di akar kuadratkan\n",
    "- RMSE menjadikan nilai MSE yang berlipat ganda normal kembali"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e3592316-4b1d-4dad-958e-bea48df5e810",
   "metadata": {},
   "source": [
    "##### RMSE vs MSE\n",
    "\n",
    "Mana yang digunakan antara MSE dan RMSE ?\n",
    "- Jika kasus yang dihadapi terdapat outlier maka MAE lebih baik dari RMSE\n",
    "- Jika membandingkan dua model regresi yang berbeda maka gunakan RMSE \n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "edbced2e-0b1b-4513-9fdf-f4620fe095b1",
   "metadata": {},
   "source": [
    "##### 8.4 R-Squared Value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "8b38eb43-b363-4e4f-bf32-5bb04078378f",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean R^2  Squared Value 0.9767394334918118\n"
     ]
    }
   ],
   "source": [
    "print('Mean R^2  Squared Value', metrics.r2_score(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "26b275b3-8d12-47fb-81d2-d66546004e89",
   "metadata": {},
   "source": [
    "- R-squared adalah nilai yang memiliki rentang 0-1 untuk menjelaskan besarnya variasi variable independen terhadap variable dependen\n",
    "- semakin besar nilai R-square maka semakin baik model yang dibuat \n",
    "- contoh pada nilai diatas sebesar 97% dari variable dependen dapat dijelaskan oleh variable independen. artinya model regresi yang dibuat sangat baik dalam memprediksi data baru\n",
    "- namun kekurangan R-square yaitu semakin banyak prediktor maka semakin tinggi nilainya, padahal hubungan antar variablenya belum jelas. sehingga mudah untuk meningkatkan R-square karena hanya perlu menambahkan kolom prediktor saja. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "547d0601-7ca7-4970-96c7-f0a37ef9697b",
   "metadata": {},
   "source": [
    "#### 9. Visualisasi Hasil model Regresi"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "5e16a86e-65e8-42f7-8054-aa760917c3c4",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(x_test,y_test)\n",
    "plt.plot(x_test,y_pred, color='red', linewidth=3)\n",
    "plt.xlabel('Hours')\n",
    "plt.ylabel('Scores')\n",
    "plt.title('Linear Regression')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d35549aa-bd12-4851-a404-5c46ed61e5e2",
   "metadata": {},
   "source": [
    "**insight:**\n",
    "- terlihat bahwa seluruh data uji tidak berbeda jauh dari garis gradien sehingga secara garis besar model yang dibuat memiliki prediksi yang sangat akurat dan baik"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "6ab7e77b-19c8-4f99-8d3d-8a2ae60b26ec",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Residual')"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Residual plot\n",
    "plt.scatter(regressor.predict(x_test), regressor.predict(x_test) - y_test, c='g', s = 40)\n",
    "plt.hlines(y=0, xmin=0, xmax=100)\n",
    "plt.title('Residual plot')\n",
    "plt.ylabel('Residual')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "92d45662-b815-4aed-ada6-e895f5a35745",
   "metadata": {},
   "source": [
    "**insight:**\n",
    "- dari scatter plot diatas diketahui bahwa model regresi tidak terjebak dalam heteroskedastisitas karena garisnya tidak mengikuti besarnya nilai prediksi. artinya model layak untuk memprediksi data baru"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b9a408dc-7433-4a24-93ce-2c31105f3365",
   "metadata": {
    "tags": []
   },
   "source": [
    "###### Conclusion \n",
    "\n",
    "Tahapan SLR di python\n",
    "1. Import Library\n",
    "2. Membaca Data\n",
    "3. Data pre-processing - EDA\n",
    "4. Menentukan variable X dan variable Y\n",
    "5. Splitting Data\n",
    "6. Melakukan Pemodelan\n",
    "7. Melakukan Prediksi Data\n",
    "8. Uji Performa Model\n",
    "9. Visualisasi hasil regresi'\n",
    "10. Analisis residual"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "vulcan_da",
   "language": "python",
   "name": "vulcan_da"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}