Model Prediksi cuaca New.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "3929abb0",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.datasets import make_classification\n",
    "from sklearn.utils import resample"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "9aa635af",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"prediksicuaca2.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "da8f5662",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>precipitation</th>\n",
       "      <th>temp_max</th>\n",
       "      <th>temp_min</th>\n",
       "      <th>wind</th>\n",
       "      <th>weather</th>\n",
       "      <th>Prediction</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1/1/2012</td>\n",
       "      <td>0.0</td>\n",
       "      <td>12.8</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.7</td>\n",
       "      <td>drizzle</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1/2/2012</td>\n",
       "      <td>10.9</td>\n",
       "      <td>10.6</td>\n",
       "      <td>2.8</td>\n",
       "      <td>4.5</td>\n",
       "      <td>rain</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1/3/2012</td>\n",
       "      <td>0.8</td>\n",
       "      <td>11.7</td>\n",
       "      <td>7.2</td>\n",
       "      <td>2.3</td>\n",
       "      <td>rain</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1/4/2012</td>\n",
       "      <td>20.3</td>\n",
       "      <td>12.2</td>\n",
       "      <td>5.6</td>\n",
       "      <td>4.7</td>\n",
       "      <td>rain</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1/5/2012</td>\n",
       "      <td>1.3</td>\n",
       "      <td>8.9</td>\n",
       "      <td>2.8</td>\n",
       "      <td>6.1</td>\n",
       "      <td>rain</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1456</th>\n",
       "      <td>12/27/2015</td>\n",
       "      <td>8.6</td>\n",
       "      <td>4.4</td>\n",
       "      <td>1.7</td>\n",
       "      <td>2.9</td>\n",
       "      <td>rain</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1457</th>\n",
       "      <td>12/28/2015</td>\n",
       "      <td>1.5</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.7</td>\n",
       "      <td>1.3</td>\n",
       "      <td>rain</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1458</th>\n",
       "      <td>12/29/2015</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.2</td>\n",
       "      <td>0.6</td>\n",
       "      <td>2.6</td>\n",
       "      <td>fog</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1459</th>\n",
       "      <td>12/30/2015</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.6</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>3.4</td>\n",
       "      <td>sun</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1460</th>\n",
       "      <td>12/31/2015</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.6</td>\n",
       "      <td>-2.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>sun</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1461 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            date  precipitation  temp_max  temp_min  wind  weather  Prediction\n",
       "0       1/1/2012            0.0      12.8       5.0   4.7  drizzle           1\n",
       "1       1/2/2012           10.9      10.6       2.8   4.5     rain           3\n",
       "2       1/3/2012            0.8      11.7       7.2   2.3     rain           3\n",
       "3       1/4/2012           20.3      12.2       5.6   4.7     rain           3\n",
       "4       1/5/2012            1.3       8.9       2.8   6.1     rain           3\n",
       "...          ...            ...       ...       ...   ...      ...         ...\n",
       "1456  12/27/2015            8.6       4.4       1.7   2.9     rain           3\n",
       "1457  12/28/2015            1.5       5.0       1.7   1.3     rain           3\n",
       "1458  12/29/2015            0.0       7.2       0.6   2.6      fog           5\n",
       "1459  12/30/2015            0.0       5.6      -1.0   3.4      sun           4\n",
       "1460  12/31/2015            0.0       5.6      -2.1   3.5      sun           4\n",
       "\n",
       "[1461 rows x 7 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "ca2c0098",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3    641\n",
       "4    640\n",
       "5    101\n",
       "1     53\n",
       "2     26\n",
       "Name: Prediction, dtype: int64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"Prediction\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "ad9da29d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "rain       641\n",
       "sun        640\n",
       "fog        101\n",
       "drizzle     53\n",
       "snow        26\n",
       "Name: weather, dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"weather\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "86a3ec96",
   "metadata": {},
   "outputs": [],
   "source": [
    "x = df.drop([\"weather\",\"date\",\"Prediction\"],axis=1)\n",
    "y = df[\"Prediction\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "c38269e5",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import accuracy_score,classification_report,confusion_matrix\n",
    "from imblearn.over_sampling import SMOTE\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "# Melakukan oversampling dengan SMOTE\n",
    "smote = SMOTE(random_state=42) #Memangil SMOTE dengan random state 42  menghasilkan hasil yang dapat direproduksi secara konsisten. \n",
    "X_resampled, y_resampled = smote.fit_resample(x, y) # menyimpan hasil over samping tersebut pada x y resampled\n",
    "\n",
    "# Membagi data menjadi data latih dan data uji\n",
    "X_train_oversampled, X_test_oversampled, y_train_oversampled, y_test_oversampled = train_test_split(X_resampled, y_resampled, test_size=0.4, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "22cc3a77",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.7558502340093604\n",
      "Classification Report:\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           1       0.66      0.83      0.74       255\n",
      "           2       0.91      1.00      0.95       251\n",
      "           3       0.90      0.73      0.80       258\n",
      "           4       0.69      0.43      0.53       268\n",
      "           5       0.66      0.82      0.73       250\n",
      "\n",
      "    accuracy                           0.76      1282\n",
      "   macro avg       0.76      0.76      0.75      1282\n",
      "weighted avg       0.76      0.76      0.75      1282\n",
      "\n"
     ]
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 800x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Membuat model KNN\n",
    "knn_classifier = KNeighborsClassifier(n_neighbors=5)\n",
    "\n",
    "\n",
    "# Melatih model-model individu\n",
    "\n",
    "knn_classifier.fit(X_train_oversampled, y_train_oversampled)\n",
    "knn_classifier2 = knn_classifier.predict(X_test_oversampled)\n",
    "\n",
    "y_pred_oversampled = knn_classifier.predict(X_test_oversampled)\n",
    "\n",
    "# Evaluasi model\n",
    "accuracy_oversampled = accuracy_score(y_test_oversampled, y_pred_oversampled)\n",
    "report_oversampled = classification_report(y_test_oversampled, y_pred_oversampled)\n",
    "confusion_mat_oversampled = confusion_matrix(y_test_oversampled, y_pred_oversampled)\n",
    "\n",
    "print(\"Accuracy:\", accuracy_oversampled)\n",
    "print(\"Classification Report:\\n\", report_oversampled)\n",
    "\n",
    "# Memvisualisasikan Confusion Matrix\n",
    "plt.figure(figsize=(8, 6))\n",
    "sns.heatmap(confusion_mat_oversampled, annot=True, fmt=\".0f\", cmap=\"YlGnBu\")\n",
    "plt.title(\"Confusion Matrix\")\n",
    "plt.xlabel(\"Predicted Class\")\n",
    "plt.ylabel(\"True Class\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "0e6942e6",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "pickle.dump(knn_classifier, open('model.pkl','wb'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "d995c028",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['model.joblib']"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import joblib\n",
    "\n",
    "# Simpan model dengan joblib\n",
    "joblib.dump(knn_classifier, 'model.joblib')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "3f1d241b",
   "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>date</th>\n",
       "      <th>precipitation</th>\n",
       "      <th>temp_max</th>\n",
       "      <th>temp_min</th>\n",
       "      <th>wind</th>\n",
       "      <th>weather</th>\n",
       "      <th>Prediction</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1/1/2012</td>\n",
       "      <td>0.0</td>\n",
       "      <td>12.8</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.7</td>\n",
       "      <td>drizzle</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1/2/2012</td>\n",
       "      <td>10.9</td>\n",
       "      <td>10.6</td>\n",
       "      <td>2.8</td>\n",
       "      <td>4.5</td>\n",
       "      <td>rain</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1/3/2012</td>\n",
       "      <td>0.8</td>\n",
       "      <td>11.7</td>\n",
       "      <td>7.2</td>\n",
       "      <td>2.3</td>\n",
       "      <td>rain</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1/4/2012</td>\n",
       "      <td>20.3</td>\n",
       "      <td>12.2</td>\n",
       "      <td>5.6</td>\n",
       "      <td>4.7</td>\n",
       "      <td>rain</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1/5/2012</td>\n",
       "      <td>1.3</td>\n",
       "      <td>8.9</td>\n",
       "      <td>2.8</td>\n",
       "      <td>6.1</td>\n",
       "      <td>rain</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       date  precipitation  temp_max  temp_min  wind  weather  Prediction\n",
       "0  1/1/2012            0.0      12.8       5.0   4.7  drizzle           1\n",
       "1  1/2/2012           10.9      10.6       2.8   4.5     rain           3\n",
       "2  1/3/2012            0.8      11.7       7.2   2.3     rain           3\n",
       "3  1/4/2012           20.3      12.2       5.6   4.7     rain           3\n",
       "4  1/5/2012            1.3       8.9       2.8   6.1     rain           3"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "d1d44df8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='weather'>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.groupby(\"weather\")[\"wind\"].agg((\"min\",\"max\",\"mean\")).plot(kind=\"line\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "07c0f312",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1440x1080 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.groupby(\"weather\")[\"temp_max\"].agg((\"min\",\"max\",\"mean\")).plot(kind=\"bar\")\n",
    "plt.title(\"Visualisasi sederhana temperatur\")\n",
    "plt.figure(figsize=(20,15))\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "87fc2aa4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "# Buat plot dengan Seaborn\n",
    "sns.set(style=\"whitegrid\")  # Set tema plot Seaborn\n",
    "\n",
    "# Groupby dan agregasi data\n",
    "agg_data = df.groupby(\"weather\")[\"temp_max\"].agg((\"min\", \"max\", \"mean\")).reset_index()\n",
    "\n",
    "# Buat plot dengan Seaborn\n",
    "plt.figure(figsize=(10, 6))  # Set ukuran gambar\n",
    "sns.barplot(x=\"weather\", y=\"min\", data=agg_data)\n",
    "plt.title(\"Visualisasi sederhana temperatur\")\n",
    "\n",
    "# Tampilkan plot\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "c28efc0b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "# Buat plot dengan Seaborn\n",
    "sns.set(style=\"whitegrid\")  # Set tema plot Seaborn\n",
    "\n",
    "# Groupby dan agregasi data\n",
    "agg_data = df.groupby(\"weather\")[\"temp_max\"].agg((\"min\", \"max\", \"mean\")).reset_index()\n",
    "\n",
    "# Buat plot dengan Seaborn\n",
    "plt.figure(figsize=(10, 6))  # Set ukuran gambar\n",
    "width = 0.2  # Lebar setiap bar\n",
    "\n",
    "# Hitung posisi tengah untuk setiap kategori cuaca\n",
    "categories = range(len(agg_data[\"weather\"]))\n",
    "pos_min = [i - width for i in categories]\n",
    "pos_mean = categories\n",
    "pos_max = [i + width for i in categories]\n",
    "\n",
    "# Buat bar plot untuk nilai min, mean, dan max\n",
    "plt.bar(pos_min, agg_data[\"min\"], width=width, label=\"Min\", color=\"blue\")\n",
    "plt.bar(pos_mean, agg_data[\"mean\"], width=width, label=\"Mean\", color=\"green\")\n",
    "plt.bar(pos_max, agg_data[\"max\"], width=width, label=\"Max\", color=\"red\")\n",
    "\n",
    "plt.title(\"Visualisasi sederhana temperatur\")\n",
    "plt.xticks(categories, agg_data[\"weather\"])\n",
    "plt.legend()  # Menampilkan legenda\n",
    "\n",
    "# Tampilkan plot\n",
    "plt.show()\n"
   ]
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