From d2ee3b4381577599936a2f6424680357f526c098 Mon Sep 17 00:00:00 2001
From: Diego Garcia <da.garcia.corona@gmail.com>
Date: Tue, 26 Nov 2019 15:14:35 -0600
Subject: [PATCH] Project MX-PT Diego

---
 ... NOT USE_Visualizing-real-world-data.ipynb | 2492 +++++++++++++++++
 ...23_Visualizing-real-world-data-clean.ipynb |  707 +++++
 Results.pptx                                  |  Bin 0 -> 614631 bytes
 student-mat.csv                               |  396 +++
 student.txt                                   |   40 +
 5 files changed, 3635 insertions(+)
 create mode 100644 191123_OLD_DO NOT USE_Visualizing-real-world-data.ipynb
 create mode 100644 191123_Visualizing-real-world-data-clean.ipynb
 create mode 100644 Results.pptx
 create mode 100644 student-mat.csv
 create mode 100644 student.txt

diff --git a/191123_OLD_DO NOT USE_Visualizing-real-world-data.ipynb b/191123_OLD_DO NOT USE_Visualizing-real-world-data.ipynb
new file mode 100644
index 0000000..cc26b74
--- /dev/null
+++ b/191123_OLD_DO NOT USE_Visualizing-real-world-data.ipynb	
@@ -0,0 +1,2492 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Import necesary libraries:\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "%matplotlib inline"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 211,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "#Importing data file:\n",
+    "df = pd.read_csv('student-mat.csv', sep=';') #The original file has ';' as the separator\n",
+    "#Add average score column and round to 1 decimal\n",
+    "df['Avg. Score'] = ((df['G1'] + df['G2'] + df['G3'])/3).round(1)\n",
+    "#Add average score column and round to 1 decimal\n",
+    "dfcount = df\n",
+    "dfcount['Counter']=1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 217,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th>Avg. Score</th>\n",
+       "      <th>Counter</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>school</th>\n",
+       "      <th>Medu</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"5\" valign=\"top\">GP</th>\n",
+       "      <th>0</th>\n",
+       "      <td>12.533333</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>9.295238</td>\n",
+       "      <td>42</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>10.313542</td>\n",
+       "      <td>96</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>10.342045</td>\n",
+       "      <td>88</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>11.817500</td>\n",
+       "      <td>120</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"4\" valign=\"top\">MS</th>\n",
+       "      <th>1</th>\n",
+       "      <td>8.970588</td>\n",
+       "      <td>17</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>8.871429</td>\n",
+       "      <td>7</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>11.272727</td>\n",
+       "      <td>11</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>12.063636</td>\n",
+       "      <td>11</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "             Avg. Score  Counter\n",
+       "school Medu                     \n",
+       "GP     0      12.533333        3\n",
+       "       1       9.295238       42\n",
+       "       2      10.313542       96\n",
+       "       3      10.342045       88\n",
+       "       4      11.817500      120\n",
+       "MS     1       8.970588       17\n",
+       "       2       8.871429        7\n",
+       "       3      11.272727       11\n",
+       "       4      12.063636       11"
+      ]
+     },
+     "execution_count": 217,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#Pivot\n",
+    "dfcount.pivot_table(index=['school', 'Medu'], aggfunc={'Avg. Score':'mean', 'Counter':'count'})"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 218,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th>Avg. Score</th>\n",
+       "      <th>Counter</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>school</th>\n",
+       "      <th>Fedu</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"5\" valign=\"top\">GP</th>\n",
+       "      <th>0</th>\n",
+       "      <td>12.700000</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>9.564179</td>\n",
+       "      <td>67</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>10.778641</td>\n",
+       "      <td>103</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>10.762222</td>\n",
+       "      <td>90</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>11.509195</td>\n",
+       "      <td>87</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"4\" valign=\"top\">MS</th>\n",
+       "      <th>1</th>\n",
+       "      <td>8.806667</td>\n",
+       "      <td>15</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>10.233333</td>\n",
+       "      <td>12</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>10.240000</td>\n",
+       "      <td>10</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>12.666667</td>\n",
+       "      <td>9</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "             Avg. Score  Counter\n",
+       "school Fedu                     \n",
+       "GP     0      12.700000        2\n",
+       "       1       9.564179       67\n",
+       "       2      10.778641      103\n",
+       "       3      10.762222       90\n",
+       "       4      11.509195       87\n",
+       "MS     1       8.806667       15\n",
+       "       2      10.233333       12\n",
+       "       3      10.240000       10\n",
+       "       4      12.666667        9"
+      ]
+     },
+     "execution_count": 218,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dfcount.pivot_table(index=['school', 'Fedu'], aggfunc={'Avg. Score':'mean', 'Counter':'count'})"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "#Create DataFrame with only records from the school GP\n",
+    "dfGP=df[df.school == 'GP']\n",
+    "\n",
+    "#Create DataFrame with only records from the school MS\n",
+    "dfMS=df[df.school == 'MS']"
+   ]
+  },
+  {
+   "cell_type": "raw",
+   "metadata": {},
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 208,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x24a56f4ec88>"
+      ]
+     },
+     "execution_count": 208,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.boxplot(x= df['school'], y=df['Avg. Score'], hue=df['reason'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 210,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x24a584c8a20>"
+      ]
+     },
+     "execution_count": 210,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.boxplot(x= df['Walc'], y=df['Avg. Score'], hue=df['sex'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.legend.Legend at 0x24a43ac9ef0>"
+      ]
+     },
+     "execution_count": 50,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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wJkmS1JBhTJIkqSHDmCRJUkOGMUmSpIYMY5IkSQ0ZxiRJkhoyjEmSJDVkGJMkSWrIMCZJktSQYUySJKkhw5gkSVJDhjFJkqSGDGOSJEkNGcYkSZIaMoxJkiQ1ZBiTJElqyDAmSZLUkGFMkiSpIcOYJElSQ4YxSZKkhgxjkiRJDRnGJEmSGjKMSZIkNWQYkyRJasgwJkmS1JBhTJIkqSHDmCRJUkOGMUmSpIYMY5IkSQ0ZxiRJkhoyjEmSJDVkGJMkSWrIMCZJktSQYUySJKkhw5gkSVJD81oXIEnNLFky/G0sXjz8bUjaqNkyJkmS1JBhTJIkqSHDmCRJUkOGMUmSpIYMY5IkSQ0ZxiRJkhoyjEmSJDVkP2OSNEwz0ZfZTLHPNGkobBmTJElqyDAmSZLUkGFMkiSpIcOYJElSQ4YxSZKkhgxjkiRJDRnGJEmSGjKMSZIkNWQYkyRJasgwJkmS1JBhTJIkqSHDmCRJUkNDC2NJNk/y/5J8L8nlSf6yG79NkvOTXNU9bz2sGiRJkma7YbaM/Rp4RlXtDuwBHJjkycBJwAVVtTNwQfdakiRpJA0tjFXPLd3LTbtHAYcCp3XjTwMOG1YNkiRJs91QrxlLskmSS4EbgfOr6lvAQ6tqJUD3/JAJll2cZGmSpatXrx5mmZIkSc0MNYxV1R1VtQewEHhSksffi2WXVNXeVbX3/Pnzh1ekJElSQzPya8qqugm4CDgQWJVkAUD3fONM1CBJkjQbDfPXlPOTPLgbvj/wTOBK4Gzg6G62o4GzhlWDJEnSbDdviOteAJyWZBN6oe/TVXVOkm8Cn05yDPBj4EVDrEGSJGlWG1oYq6rlwJ7jjF8DHDCs7UqSJG1M7IFfkiSpIcOYJElSQ4YxSZKkhgxjkiRJDRnGJEmSGjKMSZIkNWQYkyRJasgwJkmS1JBhTJIkqSHDmCRJUkOGMUmSpIYMY5IkSQ0ZxiRJkhoyjEmSJDVkGJMkSWrIMCZJktSQYUySJKkhw5gkSVJDA4WxJC9KslU3/GdJPp9kr+GWJkmSNPcN2jL251W1NslTgecApwEfGF5ZkiRJo2HQMHZH9/xc4ANVdRaw2XBKkiRJGh2DhrGfJPkQ8GLg3CS/cy+WlSRJ0gQGDVQvBv4NOLCqbgK2Ad44tKokSZJGxKBh7ENV9fmqugqgqlYCLx1eWZIkSaNh0DC2a/+LJJsAT5z+ciRJkkbLpGEsyVuSrAV2S3Jz91gL3AicNSMVSpIkzWGThrGq+tuq2gp4Z1U9sHtsVVXbVtVbZqhGSZKkOWveIDNV1VuSbA88sn+Zqrp4WIVJkiSNgoHCWJK3A4cDP+C3fY4VYBiTJEm6DwYKY8DzgV2q6tfDLEaSJGnUDPprymuATYdZiCRJ0igatGXsVuDSJBcAd7WOVdWfDKUqSZKkETFoGDu7e0iSJGkaDfprytOS3B94RFX915BrkiRJGhkDXTOW5HnApcBXutd7JLGlTJIk6T4a9AL+U4AnATcBVNWlwI5DqkmSJGlkDBrGbq+qX4wZV9NdjCRJ0qgZ9AL+y5IcCWySZGfgT4BvDK8sSZKk0TBoy9gJwK70urU4A7gZeN2wipIkSRoVg/6a8lbgT7uHJEmSpsmkYSzJl5jk2rCqOmTaK5IkSRohU7WMvat7fgHwMODj3esjgGuHVJMkSdLImDSMVdV/AiR5a1Xt1zfpS0kuHmplkiRJI2DQX1POT7JTVV0DkGRHYP7wypIkaYiWLBn+NhYvHv42NCcMGsZOBC5Kck33ehHwv4dSkSRJ0ggZ9NeUX+n6F3tMN+rKqvr18MqSJEkaDQOFsSQvGzNq9yRU1ceGUJMkSdLIGPQ05T59w5sDBwDfAQxjkiRJ98GgpylP6H+d5EHA6UOpSJIkaYQMejuksW4Fdp7OQiRJkkbRoNeM9ffEfz/gccBnhlWUJEnSqBj0mrF39Q3fDlxXVSuGUI+k+2Im+k6ShsljWCNo0NOUB1fVf3aPr1fViiSnDrUySZKkETBoGHvWOOMOms5CJEmSRtGkpymTvBp4DbBTkuV9k7YCvj7MwiRJkkbBVNeMfRL4MvC3wEl949dW1c+HVpUkSdKImOo05W3AT6rqiKq6jl6Hry8A9h92YZIkSaNgqjD2FXo3BSfJo4FvAjsBxyV5+3BLkyRJmvumCmNbV9VV3fDRwBldb/wHAc8damWSJEkjYKprxqpv+BnAOwGq6jdJ7hxaVZJkf1Ozj/8m0lBMFcaWJ3kX8BPg0cB5AEkePOzCJEmSRsFUpylfBfyM3nVjz66qW7vxj+PuvfJLkiRpA0zaMlZVvwLucaF+VX0D+MawipIkSRoVg/bAfw9JTpnGOiRJkkbSBocxYNm0VSFJkjSiNjiMVdWXprMQSZKkUTRQGEuyMMkXkqxOsirJ55IsHHZxkiRJc92gLWP/ApwNLAC2B77UjZMkSdJ9MGgYm19V/1JVt3ePjwLzh1iXJEnSSBg0jP0syVFJNukeRwFrhlmYJEnSKBg0jL0CeDHwU2Al8MJunCRJku6DqW6HBEBV/Rg4ZMi1SJIkjZxJw1iSkyeZXFX11kmW3QH4GPAw4E5gSVW9N8k2wKfo3WLpWuDFVfU/97JuSZKkOWGq05S/HOcBcAzw5imWvR14fVU9FngycFySxwEnARdU1c7ABd1rSZKkkTTVvSnfvX44yVbAa4E/Bs4E3j3Rct2yK+ldX0ZVrU1yBb1uMQ4F9u9mOw24iKmDnSRJ0pw05QX8SbZJ8tfAcnrhba+qenNV3TjoRpIsAvYEvgU8tAtq6wPbQyZYZnGSpUmWrl69etBNSZIkbVQmDWNJ3gl8G1gLPKGqTrm313cl2RL4HPC6qrp50OWqaklV7V1Ve8+fb5dmkiRpbpqqZez1wMOBPwNuSHJz91ibZMpglWRTekHsE1X1+W70qiQLuukLgIFb2CRJkuaaScNYVd2vqu5fVVtV1QP7HltV1QMnWzZJgH8Grqiq9/RNOhs4uhs+GjjrvrwBSZKkjdlA/YxtoKcALwW+n+TSbtz/Ad4OfDrJMcCPgRcNsQZJkqRZbWhhrKq+BmSCyQcMa7uSJEkbk0FvhyRJkqQhMIxJkiQ1ZBiTJElqyDAmSZLUkGFMkiSpIcOYJElSQ4YxSZKkhgxjkiRJDRnGJEmSGjKMSZIkNTTMe1NKWm/JktYVSJJmKVvGJEmSGjKMSZIkNWQYkyRJasgwJkmS1JBhTJIkqSHDmCRJUkOGMUmSpIYMY5IkSQ0ZxiRJkhoyjEmSJDVkGJMkSWrIMCZJktSQYUySJKkhw5gkSVJDhjFJkqSGDGOSJEkNGcYkSZIaMoxJkiQ1ZBiTJElqyDAmSZLUkGFMkiSpIcOYJElSQ4YxSZKkhgxjkiRJDRnGJEmSGjKMSZIkNWQYkyRJasgwJkmS1JBhTJIkqSHDmCRJUkOGMUmSpIYMY5IkSQ0ZxiRJkhoyjEmSJDVkGJMkSWrIMCZJktSQYUySJKkhw5gkSVJDhjFJkqSG5rUuQJrUkiXD38bixcPfhiRJE7BlTJIkqSHDmCRJUkOGMUmSpIYMY5IkSQ0ZxiRJkhoyjEmSJDVkGJMkSWrIfsakmejLTJKkCdgyJkmS1JBhTJIkqSHDmCRJUkOGMUmSpIYMY5IkSQ0ZxiRJkhoyjEmSJDVkGJMkSWrIMCZJktSQYUySJKkhw5gkSVJDhjFJkqSGhhbGknwkyY1JLusbt02S85Nc1T1vPaztS5IkbQyG2TL2UeDAMeNOAi6oqp2BC7rXkiRJI2toYayqLgZ+Pmb0ocBp3fBpwGHD2r4kSdLGYKavGXtoVa0E6J4fMtGMSRYnWZpk6erVq2esQEmSpJk0ay/gr6olVbV3Ve09f/781uVIkiQNxUyHsVVJFgB0zzfO8PYlSZJmlZkOY2cDR3fDRwNnzfD2JUmSZpVhdm1xBvBNYJckK5IcA7wdeFaSq4Bnda8lSZJG1rxhrbiqjphg0gHD2qYkSdLGZtZewC9JkjQKDGOSJEkNDe00pSRJmiOWLBn+NhYvHv42ZilbxiRJkhoyjEmSJDVkGJMkSWrIMCZJktSQYUySJKkhw5gkSVJDhjFJkqSG7GdMkqSN1Uz0/6Whs2VMkiSpIcOYJElSQ4YxSZKkhgxjkiRJDRnGJEmSGjKMSZIkNWQYkyRJash+xrRh7NtGkibn56QGZMuYJElSQ4YxSZKkhgxjkiRJDRnGJEmSGjKMSZIkNWQYkyRJasgwJkmS1JD9jM1F9m0jSdJGw5YxSZKkhgxjkiRJDRnGJEmSGjKMSZIkNWQYkyRJasgwJkmS1JBhTJIkqSHDmCRJUkN2+rqeHaVqI7Tk4sdM+zoX73fltK9zGEb5vQ+L+1Rqw5YxSZKkhgxjkiRJDRnGJEmSGjKMSZIkNWQYkyRJasgwJkmS1JBhTJIkqSH7GdO47G9Imh7+LUmaii1jkiRJDRnGJEmSGjKMSZIkNWQYkyRJasgwJkmS1JBhTJIkqSHDmCRJUkP2M6aNmn04Tb9h7NONxcby3jeWOjcmfpbMAkuWzMx2Fi+eme3cC7aMSZIkNWQYkyRJasgwJkmS1JBhTJIkqSHDmCRJUkOGMUmSpIYMY5IkSQ3Zz5gkaaNiP2uaa2wZkyRJasgwJkmS1JBhTJIkqSHDmCRJUkOGMUmSpIYMY5IkSQ0ZxiRJkhqynzFpBtgvkqTpMIzPksX7XTnt64SNq9bWbBmTJElqyDAmSZLUkGFMkiSpIcOYJElSQ03CWJIDk/xXkh8mOalFDZIkSbPBjIexJJsA7wMOAh4HHJHkcTNdhyRJ0mzQomXsScAPq+qaqvoNcCZwaIM6JEmSmktVzewGkxcCB1bVK7vXLwX2rarjx8y3GFjcvdwF+K8ZLbSd7YCftS5ilnLfjM/9Mj73y8TcN+Nzv0zMfTO+ifbLI6tq/qAradHpa8YZd49EWFVLgCXDL2d2SbK0qvZuXcds5L4Zn/tlfO6Xiblvxud+mZj7ZnzTtV9anKZcAezQ93ohcEODOiRJkpprEca+DeycZMckmwGHA2c3qEOSJKm5GT9NWVW3Jzke+DdgE+AjVXX5TNcxi43cqdl7wX0zPvfL+NwvE3PfjM/9MjH3zfimZb/M+AX8kiRJ+i174JckSWrIMCZJktSQYayBJDsk+Y8kVyS5PMlrx5ln/yS/SHJp9zi5Ra0zLcm1Sb7fveel40xPkn/obqW1PMleLeqcaUl26TsWLk1yc5LXjZlnJI6ZJB9JcmOSy/rGbZPk/CRXdc9bT7DsnL4V2wT75p1Jruz+Xr6Q5METLDvp397GbIL9ckqSn/T9vRw8wbKjeMx8qm+/XJvk0gmWncvHzLjf00P7rKkqHzP8ABYAe3XDWwH/DTxuzDz7A+e0rrXBvrkW2G6S6QcDX6bXX92TgW+1rrnBPtoE+Cm9TgVH7pgB9gP2Ai7rG/cO4KRu+CTg1An229XATsBmwPfG/t1t7I8J9s2zgXnd8Knj7Ztu2qR/exvzY4L9cgrwhimWG8ljZsz0dwMnj+AxM+739LA+a2wZa6CqVlbVd7rhtcAVwPZtq9poHAp8rHouAR6cZEHrombYAcDVVXVd60JaqKqLgZ+PGX0ocFo3fBpw2DiLzvlbsY23b6rqvKq6vXt5Cb2+HUfKBMfMIEbymFkvSYAXA2fMaFGzwCTf00P5rDGMNZZkEbAn8K1xJv9eku8l+XKSXWe0sHYKOC/Jsu6WWGNtD1zf93oFoxdkD2fiD8dRPGYAHlpVK6H3IQo8ZJx5PHbgFfRalscz1d/eXHR8d/r2IxOcbhr1Y+ZpwKqqumqC6SNxzIz5nh7KZ41hrKEkWwKfA15XVTePmfwdeqehdgf+L/DFma6vkadU1V7AQcBxSfYbM32g22nNVV1HyYcAnxln8qgeM4Ma9WPnT4HbgU9MMMtUf3tzzQeARwF7ACvpnY4ba6SPGeAIJm8Vm/PHzBTf0xMuNs64SY8bw1gjSTal9w/8iar6/NjpVXVzVd3SDZ8LbJpkuxkuc8ZV1Q3d843AF+g19/Yb9dtpHQR8p6pWjZ0wqsdMZ9X609Xd843jzDOyx06So4E/AF5S3UUtYw3wtzenVNWqqrqjqu4E/onx3+8oHzPzgM2exJUAAAQ2SURBVBcAn5ponrl+zEzwPT2UzxrDWAPdefh/Bq6oqvdMMM/DuvlI8iR6/1ZrZq7KmZdkiyRbrR+md+HxZWNmOxt4WferyicDv1jfZDwiJvyf6igeM33OBo7uho8GzhpnnpG8FVuSA4E3A4dU1a0TzDPI396cMuZa0+cz/vsdyWOm80zgyqpaMd7EuX7MTPI9PZzPmta/WBjFB/BUek2Wy4FLu8fBwLHAsd08xwOX0/sVxiXA77euewb2y07d+/1e997/tBvfv18CvI/eL1W+D+zduu4Z3D8PoBeuHtQ3buSOGXphdCVwG73/gR4DbAtcAFzVPW/Tzftw4Ny+ZQ+m96uoq9cfX3PpMcG++SG961fWf9Z8cOy+mehvb648Jtgvp3efIcu7L8oFHjO9fdON/+j6z5a+eUfpmJnoe3oonzXeDkmSJKkhT1NKkiQ1ZBiTJElqyDAmSZLUkGFMkiSpIcOYJElSQ4YxSbNSkucnqSSPGdL6d0lyUZJLk1yRZMkwtiNJUzGMSZqtjgC+Rq/DxGH4B+DvqmqPqnosvVtI3SdJNrnvZUkaNYYxSbNOdz+4p9DrnPPwvvGfSnJw3+uPJvnDJA9I8unups+fSvKtJHtPsZkF9Dq5BKCqvt+tc5Mk70ry/W59J3TjD0jy3W78R5L8Tjf+2iQnJ/ka8KIkj0ryle7myV8dVsuepLnDMCZpNjoM+EpV/Tfw8yR7dePPBP4I7rpp+gHAucBrgP+pqt2AtwJPHGAbfwdcmOTLSU5M8uBu/GJgR2DPbn2fSLI5vR7J/6iqngDMA17dt651VfXUqjoTWAKcUFVPBN4AvH/DdoGkUWEYkzQbHUEveNE9H9ENfxl4RtcqdRBwcVX9it6tS84EqKrL6N3CZFJV9S/AY4HPAPsDl3TrfSa9Wwbd3s33c2AX4EddOAQ4Ddivb3Wfgrta9H4f+EySS4EP0WuBk6QJzWtdgCT1S7It8Azg8UkK2ASoJG+qqnVJLgKeQ6+FbP1N07Mh26qqG4CPAB9Jchnw+G5dY+8TN9X6f9k93w+4qar22JB6JI0mW8YkzTYvBD5WVY+sqkVVtQPwI3qtX9BrAftj4GnAv3Xjvga8GCDJ44AnTLWRJAcm2bQbfhi9GwD/BDgPODbJvG7aNsCVwKIkj+4Wfynwn2PXWVU3Az9K8qJu2STZ/V6+f0kjxjAmabY5AvjCmHGfA47shs+jd4rw36vqN9249wPzkywH3kzvNOUvAJJ8eIKL+Z8NXJbke/RC3Rur6qfAh4EfA8u7aUdW1Tp6AfAzSb4P3Al8cIL6XwIc0y17OXDovXr3kkZOqsa2xkvSxqXrUmLT7jTmo4ALgN/tC2uSNGt5zZikueABwH90px0DvNogJmljYcuYJElSQ14zJkmS1JBhTJIkqSHDmCRJUkOGMUmSpIYMY5IkSQ39f77B9Sjb/9d9AAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<Figure size 720x504 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(10,7)) #Changes the size\n",
+    "sns.distplot(dfGP['Avg. Score'], kde=False, color='red', bins=20, label = 'GP')\n",
+    "sns.distplot(dfMS['Avg. Score'], kde=False, color='blue', bins=20, label = 'MS')\n",
+    "plt.title('Histogram of Avg. Final Score')\n",
+    "plt.xlabel('Avg. Score')\n",
+    "plt.ylabel('No.Students')\n",
+    "plt.legend()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0, 0.5, 'No. Students')"
+      ]
+     },
+     "execution_count": 46,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 720x504 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig,(ax1, ax2) = plt.subplots(1,2, figsize=(10,7), sharex=True) #Con este parametro se indica el número de filas (2) y el número de gráficas por fila (3)\n",
+    "sns.distplot(dfGP['Avg. Score'], kde=False, color='red', bins=20, label = 'GP', ax=ax1, ).set_ylabel('No. Students')\n",
+    "sns.distplot(dfMS['Avg. Score'], kde=False, color='blue', bins=20, label = 'MS', ax=ax2).set_ylabel('No. Students')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 260,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1080x504 with 3 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig,(ax1,ax2,ax3) = plt.subplots(1,3, figsize=(15,7), sharex=False) #Con sharex se comparte el mismo eje x\n",
+    "sns.countplot(x='school', data=df, ax=ax1).set_ylabel('No. Students', fontsize=15)\n",
+    "sns.countplot(x='age', data=df, hue='school',ax=ax2).set_ylabel('No. Students', fontsize=15)\n",
+    "sns.distplot(dfGP['Avg. Score'], kde=False, color='red', bins=20, label = 'GP', ax=ax3).set_ylabel('No. Students', fontsize=15)\n",
+    "sns.distplot(dfMS['Avg. Score'], kde=False, color='blue', bins=20, label = 'MS', ax=ax3)\n",
+    "ax1.title.set_text('Population Distribution')\n",
+    "ax2.title.set_text('Population Distribution')\n",
+    "ax3.title.set_text('Average Score Distribution')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 283,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "\"sns.countplot(x='school', data=df, ax=ax1).set_ylabel('No. Students', fontsize=15)\\n\\nsns.countplot(x='age', data=df, hue='school',ax=ax2).set_ylabel('No. Students', fontsize=15)\\nsns.distplot(dfGP['Avg. Score'], kde=False, color='red', bins=20, label = 'GP', ax=ax3).set_ylabel('No. Students', fontsize=15)\\nsns.distplot(dfMS['Avg. Score'], kde=False, color='blue', bins=20, label = 'MS', ax=ax3)\\nax1.title.set_text('Population Distribution')\\nax2.title.set_text('Population Distribution')\\nax3.title.set_text('Average Score Distribution')\""
+      ]
+     },
+     "execution_count": 283,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1080x504 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 410.875x360 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig,(ax1,ax2) = plt.subplots(1,2, figsize=(15,7), sharex=False) #Con sharex se comparte el mismo eje x\n",
+    "sns.scatterplot(x='studytime', y='Avg. Score', data = df, hue='school', ax=ax2)\n",
+    "sns.boxplot(x='studytime', y='Avg. Score', data = df, hue='school',ax=ax1)\n",
+    "sns.lmplot(x='studytime', y='Avg. Score', data = df, hue='school')\n",
+    "\n",
+    "'''sns.countplot(x='school', data=df, ax=ax1).set_ylabel('No. Students', fontsize=15)\n",
+    "\n",
+    "sns.countplot(x='age', data=df, hue='school',ax=ax2).set_ylabel('No. Students', fontsize=15)\n",
+    "sns.distplot(dfGP['Avg. Score'], kde=False, color='red', bins=20, label = 'GP', ax=ax3).set_ylabel('No. Students', fontsize=15)\n",
+    "sns.distplot(dfMS['Avg. Score'], kde=False, color='blue', bins=20, label = 'MS', ax=ax3)\n",
+    "ax1.title.set_text('Population Distribution')\n",
+    "ax2.title.set_text('Population Distribution')\n",
+    "ax3.title.set_text('Average Score Distribution')'''"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 265,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead tr th {\n",
+       "        text-align: left;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead tr:last-of-type th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr>\n",
+       "      <th></th>\n",
+       "      <th colspan=\"8\" halign=\"left\">Dalc</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>age</th>\n",
+       "      <th>15</th>\n",
+       "      <th>16</th>\n",
+       "      <th>17</th>\n",
+       "      <th>18</th>\n",
+       "      <th>19</th>\n",
+       "      <th>20</th>\n",
+       "      <th>21</th>\n",
+       "      <th>22</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>sex</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>F</th>\n",
+       "      <td>1.315789</td>\n",
+       "      <td>1.240741</td>\n",
+       "      <td>1.241379</td>\n",
+       "      <td>1.232558</td>\n",
+       "      <td>1.285714</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>M</th>\n",
+       "      <td>1.386364</td>\n",
+       "      <td>1.640000</td>\n",
+       "      <td>1.925000</td>\n",
+       "      <td>1.897436</td>\n",
+       "      <td>1.700000</td>\n",
+       "      <td>2.5</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>5.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "         Dalc                                                       \n",
+       "age        15        16        17        18        19   20   21   22\n",
+       "sex                                                                 \n",
+       "F    1.315789  1.240741  1.241379  1.232558  1.285714  1.0  NaN  NaN\n",
+       "M    1.386364  1.640000  1.925000  1.897436  1.700000  2.5  3.0  5.0"
+      ]
+     },
+     "execution_count": 265,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "s_age=df.pivot_table(index=['sex'], aggfunc = {'Dalc':'mean'}, columns='age')\n",
+    "s_age\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 79,
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>school</th>\n",
+       "      <th>age</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>GP</td>\n",
+       "      <td>18</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>GP</td>\n",
+       "      <td>17</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>GP</td>\n",
+       "      <td>15</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>GP</td>\n",
+       "      <td>15</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>GP</td>\n",
+       "      <td>16</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>390</th>\n",
+       "      <td>MS</td>\n",
+       "      <td>20</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>391</th>\n",
+       "      <td>MS</td>\n",
+       "      <td>17</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>392</th>\n",
+       "      <td>MS</td>\n",
+       "      <td>21</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>393</th>\n",
+       "      <td>MS</td>\n",
+       "      <td>18</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>394</th>\n",
+       "      <td>MS</td>\n",
+       "      <td>19</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>395 rows × 2 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    school  age\n",
+       "0       GP   18\n",
+       "1       GP   17\n",
+       "2       GP   15\n",
+       "3       GP   15\n",
+       "4       GP   16\n",
+       "..     ...  ...\n",
+       "390     MS   20\n",
+       "391     MS   17\n",
+       "392     MS   21\n",
+       "393     MS   18\n",
+       "394     MS   19\n",
+       "\n",
+       "[395 rows x 2 columns]"
+      ]
+     },
+     "execution_count": 79,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df[['school','age']]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0, 0.5, 'Daily Alcohol Consumption')"
+      ]
+     },
+     "execution_count": 52,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1080x504 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(15,7)) #Changes the size\n",
+    "sns.lineplot(x='age', y='Dalc', data=df, hue='sex', ci=None) #The x is the index, not the name of the column since this is a pivot\n",
+    "plt.ylim([0,6])\n",
+    "plt.title('Daily Alcohol Consumption vs Age and Sex')\n",
+    "plt.xlabel('Age')\n",
+    "plt.ylabel('Daily Alcohol Consumption')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 263,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0, 0.5, 'Daily Alcohol Consumption')"
+      ]
+     },
+     "execution_count": 263,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1080x504 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(15,7)) #Changes the size\n",
+    "sns.barplot(x='age', y='Dalc', data=df, hue='sex') #The x is the index, not the name of the column since this is a pivot\n",
+    "plt.ylim([0,6])\n",
+    "plt.title('Daily Alcohol Consumption vs Age and Sex')\n",
+    "plt.xlabel('Age')\n",
+    "plt.ylabel('Daily Alcohol Consumption')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 301,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0, 0.5, 'Consumo Alcohol en fin de Semana')"
+      ]
+     },
+     "execution_count": 301,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1080x504 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(15,7)) #Changes the size\n",
+    "sns.lineplot(x='age', y='Walc', data=df, hue='sex', ci=None) #The x is the index, not the name of the column since this is a pivot\n",
+    "plt.ylim([0,6])\n",
+    "plt.title('Consumo alcohol por edad y sexo')\n",
+    "plt.xlabel('Edad')\n",
+    "plt.ylabel('Consumo Alcohol en fin de Semana')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 304,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0, 0.5, 'Avg. Score')"
+      ]
+     },
+     "execution_count": 304,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1080x504 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(15,7)) #Changes the size\n",
+    "sns.lineplot(x='Dalc', y='Avg. Score', data=df, hue='sex', ci=None) #The x is the index, not the name of the column since this is a pivot\n",
+    "plt.ylim([0,20])\n",
+    "plt.title('Calificaciones por consumo de alcohol entre semana')\n",
+    "plt.xlabel('Consumo alcohol entre semana')\n",
+    "plt.ylabel('Avg. Score')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 105,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "x = list(df['age'])\n",
+    "y = list(df['Dalc'])\n",
+    "z = list(df['Walc'])\n",
+    "\n",
+    "plt.scatter(x, y, label=\"Dalc\")\n",
+    "plt.scatter(x, z, label=\"Walc\")\n",
+    "\n",
+    "plt.xlabel('Age')\n",
+    "plt.ylabel('Alcohol Consumption')\n",
+    "\n",
+    "plt.title(\"Linear vs Non Linear Plots\")\n",
+    "plt.legend()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 114,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x220e4c04400>"
+      ]
+     },
+     "execution_count": 114,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "test=df.pivot_table(index=['school'], aggfunc={'Dalc':'mean', 'Walc':'mean'})\n",
+    "test.plot(kind='bar')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 91,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x24a4c763390>"
+      ]
+     },
+     "execution_count": 91,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "test=df.pivot_table(index=['school','age'], aggfunc={'Counter':'count'})\n",
+    "test.plot(kind='bar')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 219,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x24a585b30f0>"
+      ]
+     },
+     "execution_count": 219,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 720x504 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(10,7))\n",
+    "sns.countplot(x='age', data=df, hue='school')\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 220,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x24a588350f0>"
+      ]
+     },
+     "execution_count": 220,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.countplot(x='school', data=df)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 191,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>age</th>\n",
+       "      <th>Medu</th>\n",
+       "      <th>Fedu</th>\n",
+       "      <th>traveltime</th>\n",
+       "      <th>studytime</th>\n",
+       "      <th>failures</th>\n",
+       "      <th>famrel</th>\n",
+       "      <th>freetime</th>\n",
+       "      <th>goout</th>\n",
+       "      <th>Dalc</th>\n",
+       "      <th>Walc</th>\n",
+       "      <th>health</th>\n",
+       "      <th>absences</th>\n",
+       "      <th>G1</th>\n",
+       "      <th>G2</th>\n",
+       "      <th>G3</th>\n",
+       "      <th>Avg. Score</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>age</th>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>-0.163658</td>\n",
+       "      <td>-0.163438</td>\n",
+       "      <td>0.070641</td>\n",
+       "      <td>-0.004140</td>\n",
+       "      <td>0.243665</td>\n",
+       "      <td>0.053940</td>\n",
+       "      <td>0.016434</td>\n",
+       "      <td>0.126964</td>\n",
+       "      <td>0.131125</td>\n",
+       "      <td>0.117276</td>\n",
+       "      <td>-0.062187</td>\n",
+       "      <td>0.175230</td>\n",
+       "      <td>-0.064081</td>\n",
+       "      <td>-0.143474</td>\n",
+       "      <td>-0.161579</td>\n",
+       "      <td>-0.134355</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Medu</th>\n",
+       "      <td>-0.163658</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.623455</td>\n",
+       "      <td>-0.171639</td>\n",
+       "      <td>0.064944</td>\n",
+       "      <td>-0.236680</td>\n",
+       "      <td>-0.003914</td>\n",
+       "      <td>0.030891</td>\n",
+       "      <td>0.064094</td>\n",
+       "      <td>0.019834</td>\n",
+       "      <td>-0.047123</td>\n",
+       "      <td>-0.046878</td>\n",
+       "      <td>0.100285</td>\n",
+       "      <td>0.205341</td>\n",
+       "      <td>0.215527</td>\n",
+       "      <td>0.217147</td>\n",
+       "      <td>0.223854</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Fedu</th>\n",
+       "      <td>-0.163438</td>\n",
+       "      <td>0.623455</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>-0.158194</td>\n",
+       "      <td>-0.009175</td>\n",
+       "      <td>-0.250408</td>\n",
+       "      <td>-0.001370</td>\n",
+       "      <td>-0.012846</td>\n",
+       "      <td>0.043105</td>\n",
+       "      <td>0.002386</td>\n",
+       "      <td>-0.012631</td>\n",
+       "      <td>0.014742</td>\n",
+       "      <td>0.024473</td>\n",
+       "      <td>0.190270</td>\n",
+       "      <td>0.164893</td>\n",
+       "      <td>0.152457</td>\n",
+       "      <td>0.175546</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>traveltime</th>\n",
+       "      <td>0.070641</td>\n",
+       "      <td>-0.171639</td>\n",
+       "      <td>-0.158194</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>-0.100909</td>\n",
+       "      <td>0.092239</td>\n",
+       "      <td>-0.016808</td>\n",
+       "      <td>-0.017025</td>\n",
+       "      <td>0.028540</td>\n",
+       "      <td>0.138325</td>\n",
+       "      <td>0.134116</td>\n",
+       "      <td>0.007501</td>\n",
+       "      <td>-0.012944</td>\n",
+       "      <td>-0.093040</td>\n",
+       "      <td>-0.153198</td>\n",
+       "      <td>-0.117142</td>\n",
+       "      <td>-0.127630</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>studytime</th>\n",
+       "      <td>-0.004140</td>\n",
+       "      <td>0.064944</td>\n",
+       "      <td>-0.009175</td>\n",
+       "      <td>-0.100909</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>-0.173563</td>\n",
+       "      <td>0.039731</td>\n",
+       "      <td>-0.143198</td>\n",
+       "      <td>-0.063904</td>\n",
+       "      <td>-0.196019</td>\n",
+       "      <td>-0.253785</td>\n",
+       "      <td>-0.075616</td>\n",
+       "      <td>-0.062700</td>\n",
+       "      <td>0.160612</td>\n",
+       "      <td>0.135880</td>\n",
+       "      <td>0.097820</td>\n",
+       "      <td>0.134373</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>failures</th>\n",
+       "      <td>0.243665</td>\n",
+       "      <td>-0.236680</td>\n",
+       "      <td>-0.250408</td>\n",
+       "      <td>0.092239</td>\n",
+       "      <td>-0.173563</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>-0.044337</td>\n",
+       "      <td>0.091987</td>\n",
+       "      <td>0.124561</td>\n",
+       "      <td>0.136047</td>\n",
+       "      <td>0.141962</td>\n",
+       "      <td>0.065827</td>\n",
+       "      <td>0.063726</td>\n",
+       "      <td>-0.354718</td>\n",
+       "      <td>-0.355896</td>\n",
+       "      <td>-0.360415</td>\n",
+       "      <td>-0.375180</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>famrel</th>\n",
+       "      <td>0.053940</td>\n",
+       "      <td>-0.003914</td>\n",
+       "      <td>-0.001370</td>\n",
+       "      <td>-0.016808</td>\n",
+       "      <td>0.039731</td>\n",
+       "      <td>-0.044337</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.150701</td>\n",
+       "      <td>0.064568</td>\n",
+       "      <td>-0.077594</td>\n",
+       "      <td>-0.113397</td>\n",
+       "      <td>0.094056</td>\n",
+       "      <td>-0.044354</td>\n",
+       "      <td>0.022168</td>\n",
+       "      <td>-0.018281</td>\n",
+       "      <td>0.051363</td>\n",
+       "      <td>0.021435</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>freetime</th>\n",
+       "      <td>0.016434</td>\n",
+       "      <td>0.030891</td>\n",
+       "      <td>-0.012846</td>\n",
+       "      <td>-0.017025</td>\n",
+       "      <td>-0.143198</td>\n",
+       "      <td>0.091987</td>\n",
+       "      <td>0.150701</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.285019</td>\n",
+       "      <td>0.209001</td>\n",
+       "      <td>0.147822</td>\n",
+       "      <td>0.075733</td>\n",
+       "      <td>-0.058078</td>\n",
+       "      <td>0.012613</td>\n",
+       "      <td>-0.013777</td>\n",
+       "      <td>0.011307</td>\n",
+       "      <td>0.003776</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>goout</th>\n",
+       "      <td>0.126964</td>\n",
+       "      <td>0.064094</td>\n",
+       "      <td>0.043105</td>\n",
+       "      <td>0.028540</td>\n",
+       "      <td>-0.063904</td>\n",
+       "      <td>0.124561</td>\n",
+       "      <td>0.064568</td>\n",
+       "      <td>0.285019</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.266994</td>\n",
+       "      <td>0.420386</td>\n",
+       "      <td>-0.009577</td>\n",
+       "      <td>0.044302</td>\n",
+       "      <td>-0.149104</td>\n",
+       "      <td>-0.162250</td>\n",
+       "      <td>-0.132791</td>\n",
+       "      <td>-0.154693</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Dalc</th>\n",
+       "      <td>0.131125</td>\n",
+       "      <td>0.019834</td>\n",
+       "      <td>0.002386</td>\n",
+       "      <td>0.138325</td>\n",
+       "      <td>-0.196019</td>\n",
+       "      <td>0.136047</td>\n",
+       "      <td>-0.077594</td>\n",
+       "      <td>0.209001</td>\n",
+       "      <td>0.266994</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.647544</td>\n",
+       "      <td>0.077180</td>\n",
+       "      <td>0.111908</td>\n",
+       "      <td>-0.094159</td>\n",
+       "      <td>-0.064120</td>\n",
+       "      <td>-0.054660</td>\n",
+       "      <td>-0.072441</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Walc</th>\n",
+       "      <td>0.117276</td>\n",
+       "      <td>-0.047123</td>\n",
+       "      <td>-0.012631</td>\n",
+       "      <td>0.134116</td>\n",
+       "      <td>-0.253785</td>\n",
+       "      <td>0.141962</td>\n",
+       "      <td>-0.113397</td>\n",
+       "      <td>0.147822</td>\n",
+       "      <td>0.420386</td>\n",
+       "      <td>0.647544</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.092476</td>\n",
+       "      <td>0.136291</td>\n",
+       "      <td>-0.126179</td>\n",
+       "      <td>-0.084927</td>\n",
+       "      <td>-0.051939</td>\n",
+       "      <td>-0.088198</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>health</th>\n",
+       "      <td>-0.062187</td>\n",
+       "      <td>-0.046878</td>\n",
+       "      <td>0.014742</td>\n",
+       "      <td>0.007501</td>\n",
+       "      <td>-0.075616</td>\n",
+       "      <td>0.065827</td>\n",
+       "      <td>0.094056</td>\n",
+       "      <td>0.075733</td>\n",
+       "      <td>-0.009577</td>\n",
+       "      <td>0.077180</td>\n",
+       "      <td>0.092476</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>-0.029937</td>\n",
+       "      <td>-0.073172</td>\n",
+       "      <td>-0.097720</td>\n",
+       "      <td>-0.061335</td>\n",
+       "      <td>-0.079903</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>absences</th>\n",
+       "      <td>0.175230</td>\n",
+       "      <td>0.100285</td>\n",
+       "      <td>0.024473</td>\n",
+       "      <td>-0.012944</td>\n",
+       "      <td>-0.062700</td>\n",
+       "      <td>0.063726</td>\n",
+       "      <td>-0.044354</td>\n",
+       "      <td>-0.058078</td>\n",
+       "      <td>0.044302</td>\n",
+       "      <td>0.111908</td>\n",
+       "      <td>0.136291</td>\n",
+       "      <td>-0.029937</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>-0.031003</td>\n",
+       "      <td>-0.031777</td>\n",
+       "      <td>0.034247</td>\n",
+       "      <td>-0.006602</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>G1</th>\n",
+       "      <td>-0.064081</td>\n",
+       "      <td>0.205341</td>\n",
+       "      <td>0.190270</td>\n",
+       "      <td>-0.093040</td>\n",
+       "      <td>0.160612</td>\n",
+       "      <td>-0.354718</td>\n",
+       "      <td>0.022168</td>\n",
+       "      <td>0.012613</td>\n",
+       "      <td>-0.149104</td>\n",
+       "      <td>-0.094159</td>\n",
+       "      <td>-0.126179</td>\n",
+       "      <td>-0.073172</td>\n",
+       "      <td>-0.031003</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.852118</td>\n",
+       "      <td>0.801468</td>\n",
+       "      <td>0.919319</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>G2</th>\n",
+       "      <td>-0.143474</td>\n",
+       "      <td>0.215527</td>\n",
+       "      <td>0.164893</td>\n",
+       "      <td>-0.153198</td>\n",
+       "      <td>0.135880</td>\n",
+       "      <td>-0.355896</td>\n",
+       "      <td>-0.018281</td>\n",
+       "      <td>-0.013777</td>\n",
+       "      <td>-0.162250</td>\n",
+       "      <td>-0.064120</td>\n",
+       "      <td>-0.084927</td>\n",
+       "      <td>-0.097720</td>\n",
+       "      <td>-0.031777</td>\n",
+       "      <td>0.852118</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.904868</td>\n",
+       "      <td>0.967886</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>G3</th>\n",
+       "      <td>-0.161579</td>\n",
+       "      <td>0.217147</td>\n",
+       "      <td>0.152457</td>\n",
+       "      <td>-0.117142</td>\n",
+       "      <td>0.097820</td>\n",
+       "      <td>-0.360415</td>\n",
+       "      <td>0.051363</td>\n",
+       "      <td>0.011307</td>\n",
+       "      <td>-0.132791</td>\n",
+       "      <td>-0.054660</td>\n",
+       "      <td>-0.051939</td>\n",
+       "      <td>-0.061335</td>\n",
+       "      <td>0.034247</td>\n",
+       "      <td>0.801468</td>\n",
+       "      <td>0.904868</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.959948</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Avg. Score</th>\n",
+       "      <td>-0.134355</td>\n",
+       "      <td>0.223854</td>\n",
+       "      <td>0.175546</td>\n",
+       "      <td>-0.127630</td>\n",
+       "      <td>0.134373</td>\n",
+       "      <td>-0.375180</td>\n",
+       "      <td>0.021435</td>\n",
+       "      <td>0.003776</td>\n",
+       "      <td>-0.154693</td>\n",
+       "      <td>-0.072441</td>\n",
+       "      <td>-0.088198</td>\n",
+       "      <td>-0.079903</td>\n",
+       "      <td>-0.006602</td>\n",
+       "      <td>0.919319</td>\n",
+       "      <td>0.967886</td>\n",
+       "      <td>0.959948</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                 age      Medu      Fedu  traveltime  studytime  failures  \\\n",
+       "age         1.000000 -0.163658 -0.163438    0.070641  -0.004140  0.243665   \n",
+       "Medu       -0.163658  1.000000  0.623455   -0.171639   0.064944 -0.236680   \n",
+       "Fedu       -0.163438  0.623455  1.000000   -0.158194  -0.009175 -0.250408   \n",
+       "traveltime  0.070641 -0.171639 -0.158194    1.000000  -0.100909  0.092239   \n",
+       "studytime  -0.004140  0.064944 -0.009175   -0.100909   1.000000 -0.173563   \n",
+       "failures    0.243665 -0.236680 -0.250408    0.092239  -0.173563  1.000000   \n",
+       "famrel      0.053940 -0.003914 -0.001370   -0.016808   0.039731 -0.044337   \n",
+       "freetime    0.016434  0.030891 -0.012846   -0.017025  -0.143198  0.091987   \n",
+       "goout       0.126964  0.064094  0.043105    0.028540  -0.063904  0.124561   \n",
+       "Dalc        0.131125  0.019834  0.002386    0.138325  -0.196019  0.136047   \n",
+       "Walc        0.117276 -0.047123 -0.012631    0.134116  -0.253785  0.141962   \n",
+       "health     -0.062187 -0.046878  0.014742    0.007501  -0.075616  0.065827   \n",
+       "absences    0.175230  0.100285  0.024473   -0.012944  -0.062700  0.063726   \n",
+       "G1         -0.064081  0.205341  0.190270   -0.093040   0.160612 -0.354718   \n",
+       "G2         -0.143474  0.215527  0.164893   -0.153198   0.135880 -0.355896   \n",
+       "G3         -0.161579  0.217147  0.152457   -0.117142   0.097820 -0.360415   \n",
+       "Avg. Score -0.134355  0.223854  0.175546   -0.127630   0.134373 -0.375180   \n",
+       "\n",
+       "              famrel  freetime     goout      Dalc      Walc    health  \\\n",
+       "age         0.053940  0.016434  0.126964  0.131125  0.117276 -0.062187   \n",
+       "Medu       -0.003914  0.030891  0.064094  0.019834 -0.047123 -0.046878   \n",
+       "Fedu       -0.001370 -0.012846  0.043105  0.002386 -0.012631  0.014742   \n",
+       "traveltime -0.016808 -0.017025  0.028540  0.138325  0.134116  0.007501   \n",
+       "studytime   0.039731 -0.143198 -0.063904 -0.196019 -0.253785 -0.075616   \n",
+       "failures   -0.044337  0.091987  0.124561  0.136047  0.141962  0.065827   \n",
+       "famrel      1.000000  0.150701  0.064568 -0.077594 -0.113397  0.094056   \n",
+       "freetime    0.150701  1.000000  0.285019  0.209001  0.147822  0.075733   \n",
+       "goout       0.064568  0.285019  1.000000  0.266994  0.420386 -0.009577   \n",
+       "Dalc       -0.077594  0.209001  0.266994  1.000000  0.647544  0.077180   \n",
+       "Walc       -0.113397  0.147822  0.420386  0.647544  1.000000  0.092476   \n",
+       "health      0.094056  0.075733 -0.009577  0.077180  0.092476  1.000000   \n",
+       "absences   -0.044354 -0.058078  0.044302  0.111908  0.136291 -0.029937   \n",
+       "G1          0.022168  0.012613 -0.149104 -0.094159 -0.126179 -0.073172   \n",
+       "G2         -0.018281 -0.013777 -0.162250 -0.064120 -0.084927 -0.097720   \n",
+       "G3          0.051363  0.011307 -0.132791 -0.054660 -0.051939 -0.061335   \n",
+       "Avg. Score  0.021435  0.003776 -0.154693 -0.072441 -0.088198 -0.079903   \n",
+       "\n",
+       "            absences        G1        G2        G3  Avg. Score  \n",
+       "age         0.175230 -0.064081 -0.143474 -0.161579   -0.134355  \n",
+       "Medu        0.100285  0.205341  0.215527  0.217147    0.223854  \n",
+       "Fedu        0.024473  0.190270  0.164893  0.152457    0.175546  \n",
+       "traveltime -0.012944 -0.093040 -0.153198 -0.117142   -0.127630  \n",
+       "studytime  -0.062700  0.160612  0.135880  0.097820    0.134373  \n",
+       "failures    0.063726 -0.354718 -0.355896 -0.360415   -0.375180  \n",
+       "famrel     -0.044354  0.022168 -0.018281  0.051363    0.021435  \n",
+       "freetime   -0.058078  0.012613 -0.013777  0.011307    0.003776  \n",
+       "goout       0.044302 -0.149104 -0.162250 -0.132791   -0.154693  \n",
+       "Dalc        0.111908 -0.094159 -0.064120 -0.054660   -0.072441  \n",
+       "Walc        0.136291 -0.126179 -0.084927 -0.051939   -0.088198  \n",
+       "health     -0.029937 -0.073172 -0.097720 -0.061335   -0.079903  \n",
+       "absences    1.000000 -0.031003 -0.031777  0.034247   -0.006602  \n",
+       "G1         -0.031003  1.000000  0.852118  0.801468    0.919319  \n",
+       "G2         -0.031777  0.852118  1.000000  0.904868    0.967886  \n",
+       "G3          0.034247  0.801468  0.904868  1.000000    0.959948  \n",
+       "Avg. Score -0.006602  0.919319  0.967886  0.959948    1.000000  "
+      ]
+     },
+     "execution_count": 191,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "cor_df=df.corr()\n",
+    "cor_df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 198,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x24a50faadd8>"
+      ]
+     },
+     "execution_count": 198,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.barplot(x='Pstatus', y='Avg. Score', data=df, hue='school')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 103,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<seaborn.axisgrid.FacetGrid at 0x24a4d130dd8>"
+      ]
+     },
+     "execution_count": 103,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 360x360 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.lmplot(x=\"absences\", y=\"Avg. Score\", data=df)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 107,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<seaborn.axisgrid.FacetGrid at 0x24a4e95c9b0>"
+      ]
+     },
+     "execution_count": 107,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 360x360 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.lmplot(x=\"studytime\", y=\"Avg. Score\", data=df)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 92,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "ValueError",
+     "evalue": "Could not interpret input 'Dalc'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
+      "\u001b[1;32m<ipython-input-92-f57537e1d000>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfigsize\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m15\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m7\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;31m#Changes the size\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[0msns\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mbarplot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mtest\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'Dalc'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mtest\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mhue\u001b[0m\u001b[1;33m=\u001b[0m \u001b[1;34m'age'\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;31m#The x is the index, not the name of the column since this is a pivot\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      3\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mylim\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m6\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      4\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtitle\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'Average Steps x Month'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mxticks\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mrotation\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m45\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mhorizontalalignment\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'right'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\seaborn\\categorical.py\u001b[0m in \u001b[0;36mbarplot\u001b[1;34m(x, y, hue, data, order, hue_order, estimator, ci, n_boot, units, orient, color, palette, saturation, errcolor, errwidth, capsize, dodge, ax, **kwargs)\u001b[0m\n\u001b[0;32m   3147\u001b[0m                           \u001b[0mestimator\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mci\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mn_boot\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0munits\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3148\u001b[0m                           \u001b[0morient\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcolor\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mpalette\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msaturation\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 3149\u001b[1;33m                           errcolor, errwidth, capsize, dodge)\n\u001b[0m\u001b[0;32m   3150\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3151\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0max\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\seaborn\\categorical.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, x, y, hue, data, order, hue_order, estimator, ci, n_boot, units, orient, color, palette, saturation, errcolor, errwidth, capsize, dodge)\u001b[0m\n\u001b[0;32m   1605\u001b[0m         \u001b[1;34m\"\"\"Initialize the plotter.\"\"\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1606\u001b[0m         self.establish_variables(x, y, hue, data, orient,\n\u001b[1;32m-> 1607\u001b[1;33m                                  order, hue_order, units)\n\u001b[0m\u001b[0;32m   1608\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mestablish_colors\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcolor\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mpalette\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msaturation\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1609\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mestimate_statistic\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mestimator\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mci\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mn_boot\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\seaborn\\categorical.py\u001b[0m in \u001b[0;36mestablish_variables\u001b[1;34m(self, x, y, hue, data, orient, order, hue_order, units)\u001b[0m\n\u001b[0;32m    153\u001b[0m                 \u001b[1;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0minput\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mstring_types\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    154\u001b[0m                     \u001b[0merr\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;34m\"Could not interpret input '{}'\"\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0minput\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 155\u001b[1;33m                     \u001b[1;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0merr\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    156\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    157\u001b[0m             \u001b[1;31m# Figure out the plotting orientation\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;31mValueError\u001b[0m: Could not interpret input 'Dalc'"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 1080x504 with 0 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(15,7)) #Changes the size\n",
+    "sns.barplot(x=test.index, y='Dalc', data=test) #The x is the index, not the name of the column since this is a pivot\n",
+    "plt.ylim([0,6])\n",
+    "plt.title('Average Steps x Month')\n",
+    "plt.xticks(rotation=45, horizontalalignment='right')\n",
+    "plt.xlabel('Month')\n",
+    "plt.ylabel('Average Steps')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 134,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th>Avg. Score</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>romantic</th>\n",
+       "      <th>age</th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"7\" valign=\"top\">no</th>\n",
+       "      <th>15</th>\n",
+       "      <td>11.917460</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16</th>\n",
+       "      <td>11.072368</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17</th>\n",
+       "      <td>10.813115</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>18</th>\n",
+       "      <td>10.255319</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19</th>\n",
+       "      <td>8.469231</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>20</th>\n",
+       "      <td>13.350000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>21</th>\n",
+       "      <td>8.300000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"7\" valign=\"top\">yes</th>\n",
+       "      <th>15</th>\n",
+       "      <td>9.189474</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16</th>\n",
+       "      <td>10.971429</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17</th>\n",
+       "      <td>10.113514</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>18</th>\n",
+       "      <td>9.968571</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19</th>\n",
+       "      <td>10.154545</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>20</th>\n",
+       "      <td>14.700000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>22</th>\n",
+       "      <td>7.300000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "              Avg. Score\n",
+       "romantic age            \n",
+       "no       15    11.917460\n",
+       "         16    11.072368\n",
+       "         17    10.813115\n",
+       "         18    10.255319\n",
+       "         19     8.469231\n",
+       "         20    13.350000\n",
+       "         21     8.300000\n",
+       "yes      15     9.189474\n",
+       "         16    10.971429\n",
+       "         17    10.113514\n",
+       "         18     9.968571\n",
+       "         19    10.154545\n",
+       "         20    14.700000\n",
+       "         22     7.300000"
+      ]
+     },
+     "execution_count": 134,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "romantic=df.pivot_table(index=['romantic', 'age'], values = ('Avg. Score'))\n",
+    "romantic"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 139,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0, 0.5, 'Average Score')"
+      ]
+     },
+     "execution_count": 139,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1080x504 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(15,7)) #Changes the size\n",
+    "sns.barplot(x=romantic.index, y='Avg. Score', data=romantic) \n",
+    "plt.ylim([5,20])\n",
+    "plt.title('Romantic Status by age vs Average Score')\n",
+    "plt.xlabel('Romantic status/age')\n",
+    "plt.ylabel('Average Score')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 141,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Avg. Score</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>higher</th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>no</th>\n",
+       "      <td>7.655000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>yes</th>\n",
+       "      <td>10.838667</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "        Avg. Score\n",
+       "higher            \n",
+       "no        7.655000\n",
+       "yes      10.838667"
+      ]
+     },
+     "execution_count": 141,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "higher=df.pivot_table(index=['higher'], values = ('Avg. Score'))\n",
+    "higher"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 142,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0, 0.5, 'Average Score')"
+      ]
+     },
+     "execution_count": 142,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1080x504 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(15,7))\n",
+    "sns.barplot(x=higher.index, y='Avg. Score', data=higher)\n",
+    "plt.ylim([5,20])\n",
+    "plt.title('Higher Education Aspirations by age vs Average Score')\n",
+    "plt.xlabel('Higher Education Aspirations/age')\n",
+    "plt.ylabel('Average Score')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 295,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0.5, 0, 'Interest in attending University')"
+      ]
+     },
+     "execution_count": 295,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1080x504 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(15,7))\n",
+    "sns.boxplot(x='higher', y='Avg. Score', data=df)\n",
+    "plt.title('Average Score based on interest of attending university')\n",
+    "plt.xlabel('Interest in attending University')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 289,
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0, 0.5, 'Average Score')"
+      ]
+     },
+     "execution_count": 289,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1080x504 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "mother=df.pivot_table(index=['Medu'], values = ('Avg. Score'))\n",
+    "father=df.pivot_table(index=['Fedu'], values = ('Avg. Score'))\n",
+    "plt.figure(figsize=(15,7))\n",
+    "sns.lineplot(x=mother.index, y='Avg. Score', data=mother, label=\"Father's Education Level\")\n",
+    "sns.lineplot(x=father.index, y='Avg. Score', data=father, label =\"Mother's Education Level\")\n",
+    "plt.ylim([5,20])\n",
+    "plt.xlim([0,4])\n",
+    "plt.xticks([0, 1,2, 3, 4])\n",
+    "plt.title(\"Average Score based on Parent's Education Level\")\n",
+    "plt.xlabel('Higher Education Aspirations/age')\n",
+    "plt.ylabel('Average Score')\n",
+    "\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 155,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0, 0.5, 'Average Score')"
+      ]
+     },
+     "execution_count": 155,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1080x504 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "mother=df.pivot_table(index=['Mjob'], values = ('Avg. Score'))\n",
+    "father=df.pivot_table(index=['Fjob'], values = ('Avg. Score'))\n",
+    "plt.figure(figsize=(15,7))\n",
+    "sns.lineplot(x=mother.index, y='Avg. Score', data=mother, label=\"Father's Job\")\n",
+    "plt.ylim([5,20])\n",
+    "plt.title('Higher Education Aspirations by age vs Average Score')\n",
+    "plt.xlabel('Higher Education Aspirations/age')\n",
+    "plt.ylabel('Average Score')\n",
+    "\n",
+    "sns.lineplot(x=father.index, y='Avg. Score', data=father, label =\"Mother's Job\")\n",
+    "plt.ylim([5,20])\n",
+    "plt.title(\"Elder's Occupation Type vs Average Score\")\n",
+    "plt.xlabel('Ocupation Type')\n",
+    "plt.ylabel('Average Score')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 159,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0, 0.5, 'Average Score')"
+      ]
+     },
+     "execution_count": 159,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1080x504 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "internet=df.pivot_table(index=['internet'], values = ('Avg. Score'))\n",
+    "plt.figure(figsize=(15,7))\n",
+    "sns.barplot(x=internet.index, y='Avg. Score', data=internet)\n",
+    "plt.ylim([5,20])\n",
+    "plt.title('Average Score controlling for internet access')\n",
+    "plt.xlabel('Internet Access')\n",
+    "plt.ylabel('Average Score')\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 306,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "([<matplotlib.axis.XTick at 0x24a5b239978>,\n",
+       "  <matplotlib.axis.XTick at 0x24a5b2988d0>,\n",
+       "  <matplotlib.axis.XTick at 0x24a5aa1c2b0>,\n",
+       "  <matplotlib.axis.XTick at 0x24a5bb5c518>],\n",
+       " <a list of 4 Text xticklabel objects>)"
+      ]
+     },
+     "execution_count": 306,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1080x504 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "traveltime=df.pivot_table(index=['traveltime'], values = ('Avg. Score'))\n",
+    "plt.figure(figsize=(15,7))\n",
+    "sns.lineplot(x=traveltime.index, y='Avg. Score', data=traveltime)\n",
+    "plt.ylim([0,15])\n",
+    "plt.title('Average Score based on travel time')\n",
+    "plt.xlabel('Travel Time')\n",
+    "plt.ylabel('Average Score')\n",
+    "plt.xticks([1,2,3,4])\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 307,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0, 0.5, 'Average Score')"
+      ]
+     },
+     "execution_count": 307,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1080x504 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "traveltime=df.pivot_table(index=['traveltime'], values = ('Avg. Score'))\n",
+    "plt.figure(figsize=(15,7))\n",
+    "sns.boxplot(x='traveltime', y='Avg. Score', data=df)\n",
+    "plt.ylim([0,15])\n",
+    "plt.title('Average Final Score based on travel time')\n",
+    "plt.xlabel('Travel Time')\n",
+    "plt.ylabel('Average Score')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "traveltime=df.pivot_table(index=['traveltime'], values = ('Avg. Score'))\n",
+    "plt.figure(figsize=(15,7))\n",
+    "sns.barplot(x=traveltime.index, y='Avg. Score', data=traveltime)\n",
+    "plt.ylim([0,15])\n",
+    "plt.title('Average Score based on travel time')\n",
+    "plt.xlabel('Travel Time')\n",
+    "plt.ylabel('Average Score')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 164,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0, 0.5, 'Average Score')"
+      ]
+     },
+     "execution_count": 164,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1080x504 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "nursery=df.pivot_table(index=['nursery'], values = ('Avg. Score'))\n",
+    "plt.figure(figsize=(15,7))\n",
+    "sns.barplot(x=nursery.index, y='Avg. Score', data=nursery)\n",
+    "plt.ylim([0,15])\n",
+    "plt.title('Average Score based on travel time')\n",
+    "plt.xlabel('Travel Time')\n",
+    "plt.ylabel('Average Score')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 168,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<seaborn.axisgrid.FacetGrid at 0x24a546b5e80>"
+      ]
+     },
+     "execution_count": 168,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 410.875x360 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.lmplot(x='studytime', y='Avg. Score', data = df, hue='school')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 266,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x24a5af10828>"
+      ]
+     },
+     "execution_count": 266,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.boxplot(x='studytime', y='Avg. Score', data = df, hue='school')\n",
+    "sns.lmplot(x='studytime', y='Avg. Score', data = df, hue='school')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 169,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0, 0.5, 'Average Score')"
+      ]
+     },
+     "execution_count": 169,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1080x504 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "paid=df.pivot_table(index=['nursery'], values = ('Avg. Score'))\n",
+    "plt.figure(figsize=(15,7))\n",
+    "sns.barplot(x=paid.index, y='Avg. Score', data=paid)\n",
+    "plt.ylim([0,15])\n",
+    "plt.title('Average Score based on travel time')\n",
+    "plt.xlabel('Travel Time')\n",
+    "plt.ylabel('Average Score')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 199,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x24a51014048>"
+      ]
+     },
+     "execution_count": 199,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.barplot(x='paid', y='Avg. Score', data=df, hue='school')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 175,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x24a54b71550>"
+      ]
+     },
+     "execution_count": 175,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "paid=df.pivot_table(index=['paid'], aggfunc={'Avg. Score':'mean'})\n",
+    "sns.barplot(x=paid.index, y='Avg. Score', data=paid)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 180,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0.5, 1.0, 'Family Relationship and Average Score')"
+      ]
+     },
+     "execution_count": 180,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.barplot(x='famrel', y='Avg. Score', data=df)\n",
+    "plt.xlabel('Family Relationship: 1-bad to 5-very good')\n",
+    "plt.title('Family Relationship and Average Score')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 185,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.legend.Legend at 0x24a56161240>"
+      ]
+     },
+     "execution_count": 185,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 720x504 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(10,7)) #Changes the size\n",
+    "sns.distplot(dfGP['Fjob'], kde=False, color='red', bins=20, label = 'GP')\n",
+    "sns.distplot(dfMS['Mjob'], kde=False, color='blue', bins=20, label = 'MS')\n",
+    "plt.title('Histogram of Avg. Final Score')\n",
+    "plt.xlabel('Avg. Score')\n",
+    "plt.ylabel('No.Students')\n",
+    "plt.legend()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 308,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x24a5a9dc320>"
+      ]
+     },
+     "execution_count": 308,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.boxplot(x='address', y='Avg. Score', data=df)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/191123_Visualizing-real-world-data-clean.ipynb b/191123_Visualizing-real-world-data-clean.ipynb
new file mode 100644
index 0000000..b6b8671
--- /dev/null
+++ b/191123_Visualizing-real-world-data-clean.ipynb
@@ -0,0 +1,707 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Importing the necesary libraries:\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "%matplotlib inline"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Dataset:"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "What: Academic Performance Dataset in Math from two highschools in Portugal.\n",
+    "Source: UCI Machine Learning Repository found in https://archive.ics.uci.edu/ml/datasets/student+performance\n",
+    "Information included: School, Sex, Age, Address (urban vs rural), Family Size, Elder's education level, Elder's job, study time, going out time, absences, access to internet, desire to attend university, partial and final score."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Loading and treating the dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#LOADING THE DATASET FOUND IN A CSV FILE\n",
+    "df = pd.read_csv('student-mat.csv', sep=';') #The original file has ';' as the separator\n",
+    "\n",
+    "#Adding an average final score column and round to 1 decimal\n",
+    "df['Avg. Score'] = ((df['G1'] + df['G2'] + df['G3'])/3).round(1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "BASIC DATA DESCRIPTION:\n",
+    "Three plots that indicate the number of students per school; the number of students per age and per school; and, the distribution of average final score by school."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1080x504 with 3 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "#Create DataFrame with only records from the school GP\n",
+    "dfGP=df[df.school == 'GP']\n",
+    "#Create DataFrame with only records from the school MS\n",
+    "dfMS=df[df.school == 'MS']\n",
+    "\n",
+    "fig,(ax1,ax2,ax3) = plt.subplots(1,3, figsize=(15,7), sharex=False) #Con sharex se comparte el mismo eje x\n",
+    "sns.countplot(x='school', data=df, ax=ax1).set_ylabel('No. Students', fontsize=15)\n",
+    "sns.countplot(x='age', data=df, hue='school',ax=ax2).set_ylabel('No. Students', fontsize=15)\n",
+    "sns.distplot(dfGP['Avg. Score'], kde=False, color='red', bins=20, label = 'GP', ax=ax3).set_ylabel('No. Students', fontsize=15)\n",
+    "sns.distplot(dfMS['Avg. Score'], kde=False, color='blue', bins=20, label = 'MS', ax=ax3)\n",
+    "ax1.title.set_text('Population Distribution')\n",
+    "ax2.title.set_text('Population Distribution')\n",
+    "ax3.title.set_text('Average Score Distribution')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Hypothesis Evaluation"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Hypothesis 1: There exists a direct corelation between the parent's education level and the academic performance of the student."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0, 0.5, 'Average Final Score')"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1080x504 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(15,7))\n",
+    "sns.lineplot(x='Medu', y='Avg. Score', data=df, ci=False, label=\"Mother's Education Level\")\n",
+    "sns.lineplot(x='Fedu', y='Avg. Score', data=df, ci=False, label=\"Father's Education Level\")\n",
+    "plt.ylim([0,20])\n",
+    "plt.xticks([0,1,2,3,4])\n",
+    "plt.title(\"Average Score based on Parent's Education Level\")\n",
+    "plt.xlabel(\"Parent's Education Level\")\n",
+    "plt.ylabel('Average Final Score')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "THERE IS NO CLEAR OR DIRECT CORRELATION. HYPOTHESIS IS INVALID"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Variation of hypothesis 1: There's a direct correlation between the student's academic performance and the parent's type of job."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0, 0.5, 'Average Final Score')"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
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\n",
+      "text/plain": [
+       "<Figure size 1080x504 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(15,7))\n",
+    "sns.lineplot(x='Mjob', y='Avg. Score', data=df, ci=False, label=\"Mother's Job\")\n",
+    "sns.lineplot(x='Fjob', y='Avg. Score', data=df, ci=False, label=\"Father's Job\")\n",
+    "plt.ylim([0,20])\n",
+    "plt.title(\"Average Score based on Parent's Education Level\")\n",
+    "plt.xlabel(\"Parent's Education Level\")\n",
+    "plt.ylabel('Average Final Score')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "THERE IS NO CLEAR OR DIRECT CORRELATION. HYPOTHESIS IS INVALID"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "HYPOTHESIS 2: THERE IS A DIRECT CORRELATION BETWEEN THE STUDENT'S ACADEMIC PERFORMANCE AND THE AMOUNT OF STUDY-TIME."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<seaborn.axisgrid.FacetGrid at 0x21a3d0b2cf8>"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1080x504 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 410.875x360 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig,(ax1,ax2) = plt.subplots(1,2, figsize=(15,7), sharex=False) #Con sharex se comparte el mismo eje x\n",
+    "sns.scatterplot(x='studytime', y='Avg. Score', data = df, hue='school', ax=ax2)\n",
+    "sns.boxplot(x='studytime', y='Avg. Score', data = df, hue='school',ax=ax1)\n",
+    "\n",
+    "sns.lmplot(x='studytime', y='Avg. Score', data = df, hue='school')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "INCONCLUSIVE: While there is no clear and direct correlation between the study time and the student's academic performance at GP, there is a slight correlation at the MS school."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "HYPOTHESIS 3: THERE IS A DIRECT CORRELATION BETWEEN THE STUDENT'S ACADEMIC PERFORMANCE AND THEIR INTEREST IN ATTENDING UNIVERSITY"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0, 0.5, 'Avg. Score')"
+      ]
+     },
+     "execution_count": 32,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1080x504 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(15,7))\n",
+    "sns.boxplot(x='higher', y='Avg. Score', data=df)\n",
+    "plt.title('Average Score based on interest of attending university')\n",
+    "plt.xlabel('Interest of attending University')\n",
+    "plt.ylabel('Avg. Score')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead tr th {\n",
+       "        text-align: left;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead tr:last-of-type th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr>\n",
+       "      <th></th>\n",
+       "      <th>mean</th>\n",
+       "      <th>min</th>\n",
+       "      <th>max</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th></th>\n",
+       "      <th>Avg. Score</th>\n",
+       "      <th>Avg. Score</th>\n",
+       "      <th>Avg. Score</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>higher</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>no</th>\n",
+       "      <td>7.655000</td>\n",
+       "      <td>1.7</td>\n",
+       "      <td>13.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>yes</th>\n",
+       "      <td>10.838667</td>\n",
+       "      <td>1.3</td>\n",
+       "      <td>19.3</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "             mean        min        max\n",
+       "       Avg. Score Avg. Score Avg. Score\n",
+       "higher                                 \n",
+       "no       7.655000        1.7       13.0\n",
+       "yes     10.838667        1.3       19.3"
+      ]
+     },
+     "execution_count": 36,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.pivot_table(index=['higher'], values = 'Avg. Score', aggfunc=[np.mean, min, max])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Valid hypothesis. Student's which have an interest in attending university had a slightly higher average final score than those who do not have such interest. While the median and the average is higher, the spread is also higher, meaning that while some students have indicated an interest in attending University, not all have taken the necessary steps to reach said goal (as seen by their low results."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead tr th {\n",
+       "        text-align: left;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead tr:last-of-type th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th>mean</th>\n",
+       "      <th>min</th>\n",
+       "      <th>max</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th>Avg. Score</th>\n",
+       "      <th>Avg. Score</th>\n",
+       "      <th>Avg. Score</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>higher</th>\n",
+       "      <th>school</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"2\" valign=\"top\">no</th>\n",
+       "      <th>GP</th>\n",
+       "      <td>7.082353</td>\n",
+       "      <td>1.7</td>\n",
+       "      <td>13.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>MS</th>\n",
+       "      <td>10.900000</td>\n",
+       "      <td>7.7</td>\n",
+       "      <td>12.7</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"2\" valign=\"top\">yes</th>\n",
+       "      <th>GP</th>\n",
+       "      <td>10.921386</td>\n",
+       "      <td>1.3</td>\n",
+       "      <td>19.3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>MS</th>\n",
+       "      <td>10.200000</td>\n",
+       "      <td>3.7</td>\n",
+       "      <td>18.7</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                    mean        min        max\n",
+       "              Avg. Score Avg. Score Avg. Score\n",
+       "higher school                                 \n",
+       "no     GP       7.082353        1.7       13.0\n",
+       "       MS      10.900000        7.7       12.7\n",
+       "yes    GP      10.921386        1.3       19.3\n",
+       "       MS      10.200000        3.7       18.7"
+      ]
+     },
+     "execution_count": 38,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.pivot_table(index=['higher','school'], values = 'Avg. Score', aggfunc=[np.mean, min, max])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0, 0.5, 'Avg. Score')"
+      ]
+     },
+     "execution_count": 33,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1080x504 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(15,7))\n",
+    "sns.boxplot(x='higher', y='Avg. Score', data=df, hue='school')\n",
+    "plt.title('Average Score based on interest of attending university')\n",
+    "plt.xlabel('Interest of attending University')\n",
+    "plt.ylabel('Avg. Score')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Overall there seems to be a direct correlation, however, when viewed on a per school basis the situation changes. In the MS school, there seems to be no correlation between the interst in attending university and a higher final score. Those that did not intend on attending university had a higher average than those that did."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Other Analysis"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Academic Performance vs Alcohol Consumption"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 61,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.axis.XTick at 0x21a439d5240>,\n",
+       " <matplotlib.axis.XTick at 0x21a439d5128>,\n",
+       " <matplotlib.axis.XTick at 0x21a435f9470>,\n",
+       " <matplotlib.axis.XTick at 0x21a43bb0b38>,\n",
+       " <matplotlib.axis.XTick at 0x21a43bb0f28>]"
+      ]
+     },
+     "execution_count": 61,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1080x504 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig,(ax1,ax2) = plt.subplots(2,1, figsize=(15,7), sharex=False) #Con sharex se comparte el mismo eje x\n",
+    "sns.lineplot(x='Dalc', y='Avg. Score', data = df, hue='sex', ci=False, ax=ax2).set_ylabel('Avg. Score', fontsize=14)\n",
+    "sns.lineplot(x='Walc', y='Avg. Score', data = df, hue='sex',ci=False, ax=ax1).set_ylabel('Avg. Score', fontsize=14)\n",
+    "ax1.set_xlabel('Weekend alcohol consumption')\n",
+    "ax2.set_xlabel('Workday alcohol consumption')\n",
+    "ax1.set_xticks([1,2,3,4,5])\n",
+    "ax2.set_xticks([1,2,3,4,5])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Alcohol Consumption by age"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 68,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0.5, 0, 'Age')"
+      ]
+     },
+     "execution_count": 68,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1080x504 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig,(ax1,ax2) = plt.subplots(2,1, figsize=(15,7), sharex=False) #Con sharex se comparte el mismo eje x\n",
+    "sns.lineplot(x='age', y='Dalc', data = df, hue='sex', ci=False, ax=ax2).set_ylabel('Alcohol Consumption', fontsize=14)\n",
+    "sns.lineplot(x='age', y='Walc', data = df, hue='sex',ci=False, ax=ax1).set_ylabel('Alcohol Consumption', fontsize=14)\n",
+    "ax1.set_xlabel('Age')\n",
+    "ax2.set_xlabel('Age')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Academic Performance based on commute time"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 76,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0, 0.5, 'Travel Time Category')"
+      ]
+     },
+     "execution_count": 76,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 720x504 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(10,7))\n",
+    "sns.boxplot(x='traveltime', y='Avg. Score', data=df, hue='school')\n",
+    "plt.title('Final Score based on Travel Time', fontsize=19)\n",
+    "plt.ylabel('Final Score', fontsize=15)\n",
+    "plt.ylabel('Travel Time Category', fontsize=15)\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/Results.pptx b/Results.pptx
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diff --git a/student-mat.csv b/student-mat.csv
new file mode 100644
index 0000000..58efc29
--- /dev/null
+++ b/student-mat.csv
@@ -0,0 +1,396 @@
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diff --git a/student.txt b/student.txt
new file mode 100644
index 0000000..6284cc7
--- /dev/null
+++ b/student.txt
@@ -0,0 +1,40 @@
+# Attributes for both student-mat.csv (Math course) and student-por.csv (Portuguese language course) datasets:
+1 school - student's school (binary: "GP" - Gabriel Pereira or "MS" - Mousinho da Silveira)
+2 sex - student's sex (binary: "F" - female or "M" - male)
+3 age - student's age (numeric: from 15 to 22)
+4 address - student's home address type (binary: "U" - urban or "R" - rural)
+5 famsize - family size (binary: "LE3" - less or equal to 3 or "GT3" - greater than 3)
+6 Pstatus - parent's cohabitation status (binary: "T" - living together or "A" - apart)
+7 Medu - mother's education (numeric: 0 - none,  1 - primary education (4th grade), 2 – 5th to 9th grade, 3 – secondary education or 4 – higher education)
+8 Fedu - father's education (numeric: 0 - none,  1 - primary education (4th grade), 2 – 5th to 9th grade, 3 – secondary education or 4 – higher education)
+9 Mjob - mother's job (nominal: "teacher", "health" care related, civil "services" (e.g. administrative or police), "at_home" or "other")
+10 Fjob - father's job (nominal: "teacher", "health" care related, civil "services" (e.g. administrative or police), "at_home" or "other")
+11 reason - reason to choose this school (nominal: close to "home", school "reputation", "course" preference or "other")
+12 guardian - student's guardian (nominal: "mother", "father" or "other")
+13 traveltime - home to school travel time (numeric: 1 - <15 min., 2 - 15 to 30 min., 3 - 30 min. to 1 hour, or 4 - >1 hour)
+14 studytime - weekly study time (numeric: 1 - <2 hours, 2 - 2 to 5 hours, 3 - 5 to 10 hours, or 4 - >10 hours)
+15 failures - number of past class failures (numeric: n if 1<=n<3, else 4)
+16 schoolsup - extra educational support (binary: yes or no)
+17 famsup - family educational support (binary: yes or no)
+18 paid - extra paid classes within the course subject (Math or Portuguese) (binary: yes or no)
+19 activities - extra-curricular activities (binary: yes or no)
+20 nursery - attended nursery school (binary: yes or no)
+21 higher - wants to take higher education (binary: yes or no)
+22 internet - Internet access at home (binary: yes or no)
+23 romantic - with a romantic relationship (binary: yes or no)
+24 famrel - quality of family relationships (numeric: from 1 - very bad to 5 - excellent)
+25 freetime - free time after school (numeric: from 1 - very low to 5 - very high)
+26 goout - going out with friends (numeric: from 1 - very low to 5 - very high)
+27 Dalc - workday alcohol consumption (numeric: from 1 - very low to 5 - very high)
+28 Walc - weekend alcohol consumption (numeric: from 1 - very low to 5 - very high)
+29 health - current health status (numeric: from 1 - very bad to 5 - very good)
+30 absences - number of school absences (numeric: from 0 to 93)
+
+# these grades are related with the course subject, Math or Portuguese:
+31 G1 - first period grade (numeric: from 0 to 20)
+31 G2 - second period grade (numeric: from 0 to 20)
+32 G3 - final grade (numeric: from 0 to 20, output target)
+
+Additional note: there are several (382) students that belong to both datasets . 
+These students can be identified by searching for identical attributes
+that characterize each student, as shown in the annexed R file.