eda-using-cufflinks-plotly.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# What are the factors affecting Graduate Admissions in America for Students?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "_cell_guid": "b1076dfc-b9ad-4769-8c92-a6c4dae69d19",
    "_uuid": "8f2839f25d086af736a60e9eeb907d3b93b6e0e5"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Admission_Predict_Ver1.1.csv', 'binary.csv']\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<script type=\"text/javascript\">window.PlotlyConfig = {MathJaxConfig: 'local'};</script><script type=\"text/javascript\">if (window.MathJax) {MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}</script><script>requirejs.config({paths: { 'plotly': ['https://cdn.plot.ly/plotly-latest.min']},});if(!window._Plotly) {require(['plotly'],function(plotly) {window._Plotly=plotly;});}</script>"
      ],
      "text/vnd.plotly.v1+html": [
       "<script type=\"text/javascript\">window.PlotlyConfig = {MathJaxConfig: 'local'};</script><script type=\"text/javascript\">if (window.MathJax) {MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}</script><script>requirejs.config({paths: { 'plotly': ['https://cdn.plot.ly/plotly-latest.min']},});if(!window._Plotly) {require(['plotly'],function(plotly) {window._Plotly=plotly;});}</script>"
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     "metadata": {},
     "output_type": "display_data"
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     "data": {
      "text/html": [
       "<script type=\"text/javascript\">window.PlotlyConfig = {MathJaxConfig: 'local'};</script><script type=\"text/javascript\">if (window.MathJax) {MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}</script><script>requirejs.config({paths: { 'plotly': ['https://cdn.plot.ly/plotly-latest.min']},});if(!window._Plotly) {require(['plotly'],function(plotly) {window._Plotly=plotly;});}</script>"
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      "text/vnd.plotly.v1+html": [
       "<script type=\"text/javascript\">window.PlotlyConfig = {MathJaxConfig: 'local'};</script><script type=\"text/javascript\">if (window.MathJax) {MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}</script><script>requirejs.config({paths: { 'plotly': ['https://cdn.plot.ly/plotly-latest.min']},});if(!window._Plotly) {require(['plotly'],function(plotly) {window._Plotly=plotly;});}</script>"
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     "metadata": {},
     "output_type": "display_data"
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   "source": [
    "# This Python 3 environment comes with many helpful analytics libraries installed\n",
    "# It is defined by the kaggle/python docker image: https://github.com/kaggle/docker-python\n",
    "# For example, here's several helpful packages to load in \n",
    "\n",
    "import numpy as np # linear algebra\n",
    "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n",
    "\n",
    "# Input data files are available in the \"../input/\" directory.\n",
    "# For example, running this (by clicking run or pressing Shift+Enter) will list the files in the input directory\n",
    "\n",
    "import os\n",
    "print(os.listdir(\"./input\"))\n",
    "\n",
    "# Any results you write to the current directory are saved as output.\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "# reading dataset\n",
    "df=pd.read_csv('./input/Admission_Predict_Ver1.1.csv')\n",
    "\n",
    "#importing plotly\n",
    "import plotly.plotly as py\n",
    "from plotly.offline import init_notebook_mode, iplot\n",
    "init_notebook_mode(connected=True)\n",
    "import plotly.graph_objs as go\n",
    "import cufflinks as cf\n",
    "cf.go_offline()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Statistics and sneek-peek into the data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "_cell_guid": "79c7e3d0-c299-4dcb-8224-4455121ee9b0",
    "_uuid": "d629ff2d2480ee46fbb7e2d37f6b5fab8052498a"
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   "outputs": [
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Serial No.</th>\n",
       "      <th>GRE Score</th>\n",
       "      <th>TOEFL Score</th>\n",
       "      <th>University Rating</th>\n",
       "      <th>SOP</th>\n",
       "      <th>LOR</th>\n",
       "      <th>CGPA</th>\n",
       "      <th>Research</th>\n",
       "      <th>Chance of Admit</th>\n",
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       "      <td>4.5</td>\n",
       "      <td>4.5</td>\n",
       "      <td>9.65</td>\n",
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       "      <td>8.67</td>\n",
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       "      <td>0.80</td>\n",
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       "      <td>3.0</td>\n",
       "      <td>8.21</td>\n",
       "      <td>0</td>\n",
       "      <td>0.65</td>\n",
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      "text/plain": [
       "   Serial No.  GRE Score  TOEFL Score  University Rating  SOP  LOR   CGPA  \\\n",
       "0           1        337          118                  4  4.5   4.5  9.65   \n",
       "1           2        324          107                  4  4.0   4.5  8.87   \n",
       "2           3        316          104                  3  3.0   3.5  8.00   \n",
       "3           4        322          110                  3  3.5   2.5  8.67   \n",
       "4           5        314          103                  2  2.0   3.0  8.21   \n",
       "\n",
       "   Research  Chance of Admit   \n",
       "0         1              0.92  \n",
       "1         1              0.76  \n",
       "2         1              0.72  \n",
       "3         1              0.80  \n",
       "4         0              0.65  "
      ]
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     "output_type": "display_data"
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     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 500 entries, 0 to 499\n",
      "Data columns (total 9 columns):\n",
      "Serial No.           500 non-null int64\n",
      "GRE Score            500 non-null int64\n",
      "TOEFL Score          500 non-null int64\n",
      "University Rating    500 non-null int64\n",
      "SOP                  500 non-null float64\n",
      "LOR                  500 non-null float64\n",
      "CGPA                 500 non-null float64\n",
      "Research             500 non-null int64\n",
      "Chance of Admit      500 non-null float64\n",
      "dtypes: float64(4), int64(5)\n",
      "memory usage: 35.2 KB\n"
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       "      <th>Serial No.</th>\n",
       "      <th>GRE Score</th>\n",
       "      <th>TOEFL Score</th>\n",
       "      <th>University Rating</th>\n",
       "      <th>SOP</th>\n",
       "      <th>LOR</th>\n",
       "      <th>CGPA</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>500.000000</td>\n",
       "      <td>500.000000</td>\n",
       "      <td>500.000000</td>\n",
       "      <td>500.000000</td>\n",
       "      <td>500.000000</td>\n",
       "      <td>500.00000</td>\n",
       "      <td>500.000000</td>\n",
       "      <td>500.000000</td>\n",
       "      <td>500.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>250.500000</td>\n",
       "      <td>316.472000</td>\n",
       "      <td>107.192000</td>\n",
       "      <td>3.114000</td>\n",
       "      <td>3.374000</td>\n",
       "      <td>3.48400</td>\n",
       "      <td>8.576440</td>\n",
       "      <td>0.560000</td>\n",
       "      <td>0.72174</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>144.481833</td>\n",
       "      <td>11.295148</td>\n",
       "      <td>6.081868</td>\n",
       "      <td>1.143512</td>\n",
       "      <td>0.991004</td>\n",
       "      <td>0.92545</td>\n",
       "      <td>0.604813</td>\n",
       "      <td>0.496884</td>\n",
       "      <td>0.14114</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>290.000000</td>\n",
       "      <td>92.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>6.800000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.34000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>125.750000</td>\n",
       "      <td>308.000000</td>\n",
       "      <td>103.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.500000</td>\n",
       "      <td>3.00000</td>\n",
       "      <td>8.127500</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.63000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>250.500000</td>\n",
       "      <td>317.000000</td>\n",
       "      <td>107.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.500000</td>\n",
       "      <td>3.50000</td>\n",
       "      <td>8.560000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.72000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>375.250000</td>\n",
       "      <td>325.000000</td>\n",
       "      <td>112.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.00000</td>\n",
       "      <td>9.040000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.82000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>500.000000</td>\n",
       "      <td>340.000000</td>\n",
       "      <td>120.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.00000</td>\n",
       "      <td>9.920000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.97000</td>\n",
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      ],
      "text/plain": [
       "       Serial No.   GRE Score  TOEFL Score  University Rating         SOP  \\\n",
       "count  500.000000  500.000000   500.000000         500.000000  500.000000   \n",
       "mean   250.500000  316.472000   107.192000           3.114000    3.374000   \n",
       "std    144.481833   11.295148     6.081868           1.143512    0.991004   \n",
       "min      1.000000  290.000000    92.000000           1.000000    1.000000   \n",
       "25%    125.750000  308.000000   103.000000           2.000000    2.500000   \n",
       "50%    250.500000  317.000000   107.000000           3.000000    3.500000   \n",
       "75%    375.250000  325.000000   112.000000           4.000000    4.000000   \n",
       "max    500.000000  340.000000   120.000000           5.000000    5.000000   \n",
       "\n",
       "            LOR         CGPA    Research  Chance of Admit   \n",
       "count  500.00000  500.000000  500.000000         500.00000  \n",
       "mean     3.48400    8.576440    0.560000           0.72174  \n",
       "std      0.92545    0.604813    0.496884           0.14114  \n",
       "min      1.00000    6.800000    0.000000           0.34000  \n",
       "25%      3.00000    8.127500    0.000000           0.63000  \n",
       "50%      3.50000    8.560000    1.000000           0.72000  \n",
       "75%      4.00000    9.040000    1.000000           0.82000  \n",
       "max      5.00000    9.920000    1.000000           0.97000  "
      ]
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    }
   ],
   "source": [
    "#General data statistics\n",
    "display(df.head())\n",
    "df.info()\n",
    "display(df.describe())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Key Highlights from dataset:\n",
    "* Average GRE Score: 316.47\n",
    "* Average TOEFL Score: 107.19\n",
    "* Average CGPA: 8.58\n",
    "* With Research: 56% of applicants"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Checking if plotly and cufflinks are working correctly"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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    "#data = [go.Histogram(x=df[\"GRE Score\"])]\n",
    "# checking if plotly and cufflinks are working correctly\n",
    "df['GRE Score'].iplot(kind=\"hist\", bins=40,title=\"GRE Score Distribution\")"
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    "layout1 = cf.Layout(\n",
    "    height=600,\n",
    "    width=800,\n",
    "    margin=dict(\n",
    "        l=200\n",
    "    )\n",
    ")\n",
    "df.corr().iplot(kind='heatmap',colorscale='spectral', title = 'Correlation between different maps', \n",
    "    layout=layout1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Following are highest correlated items with Chance of admit:\n",
    "* CGPA\n",
    "* GRE Score\n",
    "* TOEFL Score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Admit Chance']=pd.cut(np.array(df['Chance of Admit ']),3, labels=[\"bad\", \"medium\", \"good\"])\n",
    "#new_labels[:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# How good is your Acceptance chance to a University based on your Scores:\n",
    "**NOTE: You can even turn the markers on and off by clicking on the legends in the below charts**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
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    "scores_attr=['CGPA', 'GRE Score', 'TOEFL Score']\n",
    "for i in scores_attr:\n",
    "    df.iplot(x=i,y='University Rating',categories='Admit Chance',colors=['green','blue','red'],\n",
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  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# How does your SOP and LOR affect your chances of getting accepted?"
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  },
  {
   "cell_type": "code",
   "execution_count": 7,
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   "source": [
    "color_dict={'good':'seagreen','medium':'skyblue','bad':'indianred'}\n",
    "df_grouped=df.groupby(['SOP','LOR ','Admit Chance']).size().reset_index(name='counts')\n",
    "#df_grouped.head()\n",
    "df_grouped.iplot(kind='bubble',x='SOP',y='LOR ',xTitle='SOP',yTitle='LOR',title='Distribution of SOP and LOR with acceptance chances',\n",
    "                 size='counts',text='Admit Chance',colors=df_grouped['Admit Chance'].map(color_dict).tolist())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### This one is quite natural, students with good SOPs and good LORs have better acceptance chances. Although there are some exceptions."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Ideally students who are good at academics should have good GRE and TOEFL Score. Lets check this hypothesis below\n",
    "Zoom in, rotate, check the values in the 3d plot below"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
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    "studious_students=df[df['CGPA'] > 8]\n",
    "studious_students.iplot(kind='scatter3d', x='GRE Score', y='TOEFL Score',z='CGPA',mode='markers', xTitle='GRE Score',yTitle='TOEFL Score',zTitle='CGPA',\n",
    "                        title='GRE vs TOEFL vs CGPA')"
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   "source": [
    "Our hypothesis seems to be true."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Now, just to check the relationship between the SOP and LOR of students with Research. General guess would be students with research should have a good LOR and SOP."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
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       "    Plotly.newPlot(\"2bed556d-4a52-470e-8db5-d9e15c870597\", [{\"mode\": \"markers\", \"name\": \"Non Research\", \"text\": [1.0, 2.0, 2.0, 1.0, 13.0, 3.0, 2.0, 3.0, 6.0, 14.0, 6.0, 3.0, 1.0, 1.0, 3.0, 6.0, 6.0, 15.0, 5.0, 6.0, 1.0, 3.0, 4.0, 13.0, 11.0, 6.0, 1.0, 2.0, 2.0, 4.0, 14.0, 7.0, 6.0, 1.0, 1.0, 1.0, 1.0, 4.0, 7.0, 4.0, 6.0, 3.0, 4.0, 5.0, 2.0, 2.0, 1.0, 1.0, 2.0, 1.0, 1.0], \"x\": [1.0, 1.0, 1.0, 1.5, 1.5, 1.5, 1.5, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.5, 2.5, 2.5, 2.5, 2.5, 2.5, 3.0, 3.0, 3.0, 3.0, 3.0, 3.0, 3.0, 3.0, 3.5, 3.5, 3.5, 3.5, 3.5, 3.5, 3.5, 4.0, 4.0, 4.0, 4.0, 4.0, 4.0, 4.0, 4.5, 4.5, 4.5, 4.5, 5.0, 5.0, 5.0, 5.0, 5.0], \"y\": [1.0, 2.0, 2.5, 1.5, 2.0, 2.5, 3.0, 1.5, 2.0, 2.5, 3.0, 3.5, 4.0, 4.5, 1.5, 2.0, 2.5, 3.0, 3.5, 4.0, 1.5, 2.0, 2.5, 3.0, 3.5, 4.0, 4.5, 5.0, 2.0, 2.5, 3.0, 3.5, 4.0, 4.5, 5.0, 2.0, 2.5, 3.0, 3.5, 4.0, 4.5, 5.0, 3.5, 4.0, 4.5, 5.0, 3.0, 3.5, 4.0, 4.5, 5.0], \"type\": \"scatter\", \"uid\": \"c3dd3710-36bb-40a1-a617-0699ebe36451\", \"xaxis\": \"x\", \"yaxis\": \"y\"}, {\"mode\": \"markers\", \"name\": \"Research\", \"text\": [1.0, 1.0, 2.0, 2.0, 1.0, 2.0, 3.0, 1.0, 3.0, 1.0, 7.0, 1.0, 4.0, 4.0, 3.0, 3.0, 1.0, 5.0, 5.0, 10.0, 12.0, 3.0, 3.0, 1.0, 4.0, 16.0, 9.0, 16.0, 3.0, 4.0, 3.0, 6.0, 13.0, 15.0, 15.0, 11.0, 3.0, 5.0, 19.0, 16.0, 7.0, 2.0, 4.0, 11.0, 19.0], \"x\": [1.0, 1.5, 1.5, 1.5, 1.5, 2.0, 2.0, 2.0, 2.0, 2.5, 2.5, 2.5, 2.5, 2.5, 2.5, 2.5, 3.0, 3.0, 3.0, 3.0, 3.0, 3.0, 3.0, 3.5, 3.5, 3.5, 3.5, 3.5, 3.5, 3.5, 4.0, 4.0, 4.0, 4.0, 4.0, 4.0, 4.5, 4.5, 4.5, 4.5, 4.5, 5.0, 5.0, 5.0, 5.0], \"y\": [1.5, 2.5, 3.0, 3.5, 4.0, 2.5, 3.0, 3.5, 4.0, 1.5, 2.0, 2.5, 3.0, 3.5, 4.0, 4.5, 1.5, 2.0, 2.5, 3.0, 3.5, 4.0, 4.5, 2.0, 2.5, 3.0, 3.5, 4.0, 4.5, 5.0, 2.5, 3.0, 3.5, 4.0, 4.5, 5.0, 3.0, 3.5, 4.0, 4.5, 5.0, 3.5, 4.0, 4.5, 5.0], \"type\": \"scatter\", \"uid\": \"230900bb-f05e-4d13-bca1-9f5bcac6dccb\", \"xaxis\": \"x2\", \"yaxis\": \"y\"}], {\"xaxis\": {\"anchor\": \"y\", \"domain\": [0.0, 0.45], \"title\": {\"text\": \"SOP\"}}, \"yaxis\": {\"anchor\": \"x\", \"domain\": [0.0, 1.0], \"title\": {\"text\": \"LOR\"}}, \"xaxis2\": {\"anchor\": \"free\", \"domain\": [0.55, 1.0], \"position\": 0.0, \"title\": {\"text\": \"SOP\"}}, \"hovermode\": \"closest\", \"title\": {\"text\": \"SOP vs LOR for applicants with Research & Non Research experience\"}}, {\"showLink\": true, \"linkText\": \"Export to plot.ly\", \"plotlyServerURL\": \"https://plot.ly\"}); \n",
       "}\n",
       "});</script><script type=\"text/javascript\">window.addEventListener(\"resize\", function(){if (document.getElementById(\"2bed556d-4a52-470e-8db5-d9e15c870597\")) {window._Plotly.Plots.resize(document.getElementById(\"2bed556d-4a52-470e-8db5-d9e15c870597\"));};})</script>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import plotly.tools as tls\n",
    "\n",
    "fig = tls.make_subplots(rows=1, cols=2, shared_yaxes=True)\n",
    "                       \n",
    "df_non_research=df_research_grouped[df_research_grouped['Research']==0]\n",
    "df_research=df_research_grouped[df_research_grouped['Research']==1]\n",
    "fig.append_trace({'x': df_non_research.SOP, 'y': df_non_research['LOR '],'text':df_non_research['counts'],'type': 'scatter', 'name': 'Non Research','mode':'markers'}, 1, 1)\n",
    "fig.append_trace({'x': df_research.SOP, 'y': df_research['LOR '], 'type': 'scatter','text':df_research['counts'], 'name': 'Research','mode':'markers'}, 1, 2)\n",
    "fig['layout']['xaxis1'].update(title='SOP')\n",
    "fig['layout']['xaxis2'].update(title='SOP')\n",
    "fig['layout']['yaxis1'].update(title='LOR')\n",
    "fig['layout'].update(hovermode= 'closest')\n",
    "fig['layout'].update(title='SOP vs LOR for applicants with Research & Non Research experience')\n",
    "\n",
    "cf.iplot(fig)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## If you hover around and check the counts at the top right corners for both the plots. It is evident that research does help making your SOP and LOR better."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# To Summarize:\n",
    "* CPGA plays the most important role in admissions followed by GRE score and TOEFL.\n",
    "* Good SOPs and LORs are essential to get into the best universities\n",
    "* Research makes your SOP and LOR better.\n",
    "* Studious students generally tend to do good at GRE and TOEFL.\n",
    "\n",
    "> Disclamer: This is a very small dataset and the comments above are in accordance to the data given. To perform more analysis, it is essential that we have more data points and features."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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