sesi-4.ipynb

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      "LogisticRegression()\n",
      "Hasil prediksi komputer mengenai ketertarikan pengunjung saat melihat iklan\n",
      "terhadap data yang di berikan sebesar :\n",
      "82%\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "from sklearn import linear_model\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "data = pd.read_csv('Social_Network_Ads.csv')\n",
    "data = data.drop(columns=['User ID'])\n",
    "data = pd.get_dummies(data)\n",
    "x = data[[\"Age\", \"EstimatedSalary\", \"Gender_Female\", \"Gender_Male\"]]\n",
    "y = data[\"Purchased\"]\n",
    "model_LogicRegress = linear_model.LogisticRegression()\n",
    "\n",
    "# Meng scaled data agar berada pada skala yang sama/ normalisasi\n",
    "normalize = StandardScaler()\n",
    "normalize.fit(x)\n",
    "scaled_x = normalize.transform(x)\n",
    "\n",
    "# Untuk membuat dataframe dari data yang terlah di scaled\n",
    "scaled_x = pd.DataFrame(scaled_x, columns=x.columns)\n",
    "\n",
    "# Membagi data menjadi train, test menggunakan sklearn\n",
    "x_train, x_test, y_train, y_test = train_test_split(scaled_x, y, test_size=0.2, random_state=1)\n",
    "\n",
    "# latih menggunakan Logistic Regression\n",
    "model_LogicRegress.fit(x_train, y_train)\n",
    "\n",
    "if model_LogicRegress.score(x_test, y_test) > 0.75:\n",
    "    print(\"\"\"Hasil prediksi komputer mengenai ketertarikan pengunjung saat melihat iklan\n",
    "terhadap data yang di berikan sebesar :\"\"\")\n",
    "    print(str(100*(model_LogicRegress.score(x_test, y_test)))[0:2]+\"%\")\n",
    "else:\n",
    "    (\"\"\"Rata-rata pengunjung tidak tertarik saat melihat iklan\n",
    "     hasil prediksi ketertarikan pengunjung :\"\"\")\n",
    "    print(str(100*(model_LogicRegress.score(x_test, y_test)))[0:2]+\"%\")"
   ]
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