{
"cells": [
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"import numpy as np\n",
"from sklearn.model_selection import train_test_split\n",
"from sklearn.ensemble import RandomForestClassifier\n",
"from sklearn.metrics import accuracy_score"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"data = pd.read_csv(\"dataset_2_churn-bigml.csv\")"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
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   "    <th></th>\n",
   "    <th>State</th>\n",
   "    <th>Account length</th>\n",
   "    <th>Area code</th>\n",
   "    <th>Number vmail messages</th>\n",
   "    <th>Total day minutes</th>\n",

Assignment: Make a basic churn prediction model for a given dataset...