igualMN.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn import linear_model\n",
    "from sklearn import metrics\n",
    "from sklearn import model_selection\n",
    "import math"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv(\"train.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = data[[\"NU_NOTA_CN\"]].fillna(0) \n",
    "y = data[\"NU_NOTA_MT\"].fillna(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "58.05735426311826\n"
     ]
    }
   ],
   "source": [
    "X_train, X_test, y_train, y_test = model_selection.train_test_split(X, y, random_state=1)\n",
    "linreg = linear_model.LinearRegression()\n",
    "linreg.fit(X_train, y_train)\n",
    "result = linreg.predict(X_test)\n",
    "print(metrics.mean_absolute_error(y_test, result))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "9642.803695664637\n",
      "98.19777846603577\n"
     ]
    }
   ],
   "source": [
    "print(metrics.mean_squared_error(y_test, result))\n",
    "print(math.sqrt(metrics.mean_squared_error(y_test, result)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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