Machine Learning Analysis .ipynb

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   "source": [
    "#########Importing libraries##########\n",
    "import numpy as np\n",
    "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n",
    "import os\n",
    "from PIL import  Image\n",
    "import itertools\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "import io\n",
    "\n",
    "############Visualization#############\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import seaborn as sns\n",
    "import plotly.offline as py\n",
    "py.init_notebook_mode(connected=True)\n",
    "import plotly.graph_objs as go\n",
    "import plotly.tools as tls\n",
    "import plotly.figure_factory as ff\n",
    "############Machine Learning ##########\n",
    "from sklearn.datasets import make_regression\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.datasets import make_regression\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
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    {
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     "text": [
      "Percentage of null: 0.1564%\n"
     ]
    }
   ],
   "source": [
    "########Bring in the DATA!###########\n",
    "\n",
    "telcom = pd.read_csv(\"dataset/Telemarker.csv\")\n",
    "telcom.head()\n",
    "# print(telcom.dtypes)\n",
    "#######Data Cleaning Process#########\n",
    "\n",
    "#Replacing spaces with null values in total charges column\n",
    "telcom['TotalCharges'] = telcom[\"TotalCharges\"].replace(\" \",np.nan)\n",
    "print(\"Percentage of null: \" + str(round(telcom['TotalCharges'].isnull().sum()/telcom['TotalCharges'].count()*100,4))+\"%\")\n",
    "\n",
    "#Dropping null values from total charges column which contain .16% missing data\n",
    "telcom = telcom[telcom[\"TotalCharges\"].notnull()]\n",
    "telcom = telcom.reset_index()[telcom.columns]\n",
    "\n",
    "#convert to float type\n",
    "telcom[\"TotalCharges\"] = telcom[\"TotalCharges\"].astype(float)\n",
    "telcom[\"MonthlyCharges\"] = telcom[\"MonthlyCharges\"].astype(float)\n",
    "\n",
    "#replace 'No phone service' to No\n",
    "telcom[\"MultipleLines\"] = telcom[\"MultipleLines\"].replace(\"No phone service\",\"No\")\n",
    "\n",
    "#replace 'No internet service' to No for the following columns\n",
    "replace_cols = [ 'OnlineSecurity', 'OnlineBackup', 'DeviceProtection',\n",
    "                'TechSupport','StreamingTV', 'StreamingMovies']\n",
    "for i in replace_cols : \n",
    "    telcom[i]  = telcom[i].replace({'No internet service' : 'No'})\n",
    "\n",
    "#Change types of columns to boolean\n",
    "YesNoColumns = [\"Partner\",\"Dependents\",\"PhoneService\",\"MultipleLines\",\"OnlineSecurity\",\n",
    "                \"OnlineBackup\",\"DeviceProtection\",\"TechSupport\",\"StreamingTV\",\"StreamingMovies\",\n",
    "                \"PaperlessBilling\",\"Churn\"]\n",
    "# for i in YesNoColumns :\n",
    "#     telcom[i]  = telcom[i].replace({'Yes':1,'No':0})\n",
    "# BoolColumns = YesNoColumns.copy()\n",
    "# BoolColumns.append(\"SeniorCitizen\")\n",
    "# telcom[BoolColumns] = telcom[BoolColumns].astype('bool')\n",
    "# telcom.head() \n",
    "\n",
    "#Separating churn and non churn customers\n",
    "churn     = telcom[telcom[\"Churn\"] == \"Yes\"]\n",
    "not_churn = telcom[telcom[\"Churn\"] == \"No\"]\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Separating catagorical and numerical columns\n",
    "Id_col     = ['customerID']\n",
    "target_col = [\"Churn\"]\n",
    "cat_cols   = telcom.nunique()[telcom.nunique() < 6].keys().tolist()\n",
    "cat_cols   = [x for x in cat_cols if x not in target_col]\n",
    "num_cols   = [x for x in telcom.columns if x not in cat_cols + target_col + Id_col]\n",
    "\n",
    "#Binary columns with 2 values\n",
    "bin_cols   = telcom.nunique()[telcom.nunique() == 2].keys().tolist()\n",
    "\n",
    "# #Columns more than 2 values\n",
    "multi_cols = [i for i in cat_cols if i not in bin_cols]\n",
    "\n",
    "# #Label encoding Binary columns\n",
    "le = LabelEncoder()\n",
    "for i in bin_cols :\n",
    "    telcom[i] = le.fit_transform(telcom[i])\n",
    "    \n",
    "# #Duplicating columns for multi value columns\n",
    "telcom = pd.get_dummies(data = telcom,columns = multi_cols )\n",
    "# print(telcom)\n",
    "# #Scaling Numerical columns\n",
    "std = StandardScaler()\n",
    "scaled = std.fit_transform(telcom[num_cols])\n",
    "scaled = pd.DataFrame(scaled,columns=num_cols)\n",
    "# scaled.head()\n",
    "\n",
    "#dropping original values merging scaled values for numerical columns\n",
    "df_telcom_og = telcom.copy()\n",
    "telcom = telcom.drop(columns = num_cols,axis = 1)\n",
    "telcom = telcom.merge(scaled,left_index=True,right_index=True,how = \"left\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
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   "outputs": [],
   "source": [
    "#Correlation Matrix "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
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  },
  {
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  {
   "cell_type": "code",
   "execution_count": 5,
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   "source": [
    "#Model Building "
   ]
  },
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  {
   "cell_type": "code",
   "execution_count": 6,
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   "source": [
    "###Rafael###"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "#data processing for making the models \n"
   ]
  },
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   "cell_type": "code",
   "execution_count": 8,
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