mlessentials/Lab03/Exercise3.06/Exercise3_06.ipynb

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    "# Importing necessary packages\n",
    "import pandas as pd\n",
    "import altair as alt\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
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   "source": [
    "file_url = '../bank-full.csv'\n",
    "bankData = pd.read_csv(file_url, sep=\";\")"
   ]
  },
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   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.model_selection import train_test_split"
   ]
  },
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   "outputs": [],
   "source": [
    "# Finding the data types\n",
    "bankData.dtypes"
   ]
  },
  {
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   "source": [
    "# Converting all the categorical variables to dummy variables\n",
    "bankCat = pd.get_dummies(bankData[['job','marital','education','default','housing','loan','contact','month','poutcome']])"
   ]
  },
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   "outputs": [],
   "source": [
    "bankCat.shape"
   ]
  },
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   "outputs": [],
   "source": [
    "# Seperating the numerical variables\n",
    "bankNum = bankData[['age','balance','day','duration','campaign','pdays','previous']]\n",
    "bankNum.shape"
   ]
  },
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   "source": [
    "# Preparing the X variables\n",
    "X = pd.concat([bankCat, bankNum], axis=1)\n",
    "print(X.shape)\n",
    "# Preparing the Y variable\n",
    "Y = bankData['y']\n",
    "print(Y.shape)\n",
    "X.head()"
   ]
  },
  {
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   "source": [
    "# Splitting the data into train and test sets\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, Y, test_size=0.3, random_state=123)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
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   "source": [
    "# Defining the LogisticRegression function\n",
    "bankModel = LogisticRegression()\n",
    "bankModel.fit(X_train, y_train)"
   ]
  },
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   "outputs": [],
   "source": [
    "pred = bankModel.predict(X_test)\n",
    "print('Accuracy of Logisticr regression model prediction on test set: {:.2f}'.format(bankModel.score(X_test, y_test)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
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    "colab_type": "code",
    "executionInfo": {
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   },
   "outputs": [],
   "source": [
    "# Confusion Matrix for the model\n",
    "from sklearn.metrics import confusion_matrix\n",
    "confusionMatrix = confusion_matrix(y_test, pred)\n",
    "print(confusionMatrix)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 170
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 1048,
     "status": "ok",
     "timestamp": 1573002275023,
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      "displayName": "Anthony So",
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    "outputId": "955e1709-2c0b-47cc-e8d5-a62704050f43"
   },
   "outputs": [],
   "source": [
    "from sklearn.metrics import classification_report\n",
    "print(classification_report(y_test, pred))"
   ]
  }
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