hiv_aids.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h1><b>HIV/AIDS Data Analysis</b></h1>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2><b>Cleaning Process</b></h2>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p>Following are the steps we followed for data cleaning</p>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p>1. Import the libraries</p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Import the Libraries\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "%matplotlib inline\n",
    "# Algorithms\n",
    "from sklearn import linear_model\n",
    "from sklearn.linear_model import LinearRegression\n",
    "\n",
    "import seaborn as sns\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p>2.Load the dataset.</p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset=pd.read_csv('data/data.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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>Indicator Category</th>\n",
       "      <th>Indicator</th>\n",
       "      <th>Year</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Race/Ethnicity</th>\n",
       "      <th>Value</th>\n",
       "      <th>Place</th>\n",
       "      <th>BCHC Requested Methodology</th>\n",
       "      <th>Source</th>\n",
       "      <th>Methods</th>\n",
       "      <th>Notes</th>\n",
       "      <th>90% Confidence Level - Low</th>\n",
       "      <th>90% Confidence Level - High</th>\n",
       "      <th>95% Confidence Level - Low</th>\n",
       "      <th>95% Confidence Level - High</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Behavioral Health/Substance Abuse</td>\n",
       "      <td>Opioid-Related Unintentional Drug Overdose Mor...</td>\n",
       "      <td>2010</td>\n",
       "      <td>Both</td>\n",
       "      <td>All</td>\n",
       "      <td>1.7</td>\n",
       "      <td>Washington, DC</td>\n",
       "      <td>Age-Adjusted rate of opioid-related mortality ...</td>\n",
       "      <td>D.C. Department of Health, Center for Policy, ...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>This indicator is not exclusive of other drugs...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Behavioral Health/Substance Abuse</td>\n",
       "      <td>Opioid-Related Unintentional Drug Overdose Mor...</td>\n",
       "      <td>2010</td>\n",
       "      <td>Both</td>\n",
       "      <td>All</td>\n",
       "      <td>2.2</td>\n",
       "      <td>Fort Worth (Tarrant County), TX</td>\n",
       "      <td>Age-adjusted rate of opioid-related mortality ...</td>\n",
       "      <td>National Center for Health Statistics</td>\n",
       "      <td>NaN</td>\n",
       "      <td>This indicator is not exclusive of other drugs...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.5</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Behavioral Health/Substance Abuse</td>\n",
       "      <td>Opioid-Related Unintentional Drug Overdose Mor...</td>\n",
       "      <td>2010</td>\n",
       "      <td>Both</td>\n",
       "      <td>All</td>\n",
       "      <td>2.3</td>\n",
       "      <td>Oakland (Alameda County), CA</td>\n",
       "      <td>Age-adjusted rate of opioid-related mortality ...</td>\n",
       "      <td>CDC Wonder</td>\n",
       "      <td>Age-adjusted rate of opioid-related mortality ...</td>\n",
       "      <td>Data is for Alameda County. This indicator is ...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.6</td>\n",
       "      <td>3.2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Indicator Category  \\\n",
       "0  Behavioral Health/Substance Abuse   \n",
       "1  Behavioral Health/Substance Abuse   \n",
       "2  Behavioral Health/Substance Abuse   \n",
       "\n",
       "                                           Indicator  Year   Sex  \\\n",
       "0  Opioid-Related Unintentional Drug Overdose Mor...  2010  Both   \n",
       "1  Opioid-Related Unintentional Drug Overdose Mor...  2010  Both   \n",
       "2  Opioid-Related Unintentional Drug Overdose Mor...  2010  Both   \n",
       "\n",
       "  Race/Ethnicity  Value                            Place  \\\n",
       "0            All    1.7                   Washington, DC   \n",
       "1            All    2.2  Fort Worth (Tarrant County), TX   \n",
       "2            All    2.3     Oakland (Alameda County), CA   \n",
       "\n",
       "                          BCHC Requested Methodology  \\\n",
       "0  Age-Adjusted rate of opioid-related mortality ...   \n",
       "1  Age-adjusted rate of opioid-related mortality ...   \n",
       "2  Age-adjusted rate of opioid-related mortality ...   \n",
       "\n",
       "                                              Source  \\\n",
       "0  D.C. Department of Health, Center for Policy, ...   \n",
       "1              National Center for Health Statistics   \n",
       "2                                         CDC Wonder   \n",
       "\n",
       "                                             Methods  \\\n",
       "0                                                NaN   \n",
       "1                                                NaN   \n",
       "2  Age-adjusted rate of opioid-related mortality ...   \n",
       "\n",
       "                                               Notes  \\\n",
       "0  This indicator is not exclusive of other drugs...   \n",
       "1  This indicator is not exclusive of other drugs...   \n",
       "2  Data is for Alameda County. This indicator is ...   \n",
       "\n",
       "   90% Confidence Level - Low  90% Confidence Level - High  \\\n",
       "0                         NaN                          NaN   \n",
       "1                         NaN                          NaN   \n",
       "2                         NaN                          NaN   \n",
       "\n",
       "   95% Confidence Level - Low  95% Confidence Level - High  \n",
       "0                         NaN                          NaN  \n",
       "1                         1.5                          3.0  \n",
       "2                         1.6                          3.2  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p>Above we saw the column names and we might need to fix the spaces in the column names. In order to change that we\n",
    "need to first know what are the actual names of the columns.</p>\n",
    "\n",
    "<p>We do that using the pandas function <i>columns</i> to list all the columns</p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Indicator Category', 'Indicator', 'Year', 'Sex', 'Race/Ethnicity',\n",
       "       'Value', 'Place', 'BCHC Requested Methodology', 'Source', 'Methods',\n",
       "       'Notes', '90% Confidence Level - Low', '90% Confidence Level - High',\n",
       "       '95% Confidence Level - Low', '95% Confidence Level - High'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p>Now we rename the columns</p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset.rename(columns={'Indicator Category':'indicator_category','Indicator':'indicator','Year':'year', 'Sex':'sex', 'Race/Ethnicity':'race_ethnicity',\n",
    "       'Value':'value', 'Place':'place', 'BCHC Requested Methodology':'bchc_req_meth', 'Source':'source', 'Methods':'methods',\n",
    "       'Notes':'notes', '90% Confidence Level - Low':'90pc_con_lvl-low', '90% Confidence Level - High':'90pc_con_lvl-high',\n",
    "       '95% Confidence Level - Low':'95pc_con_lvl-low','95% Confidence Level - High':'95pc_con_lvl-high'},inplace = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p>3.Now we need to filter the data according to the indicator category. We use one of the values \"Cancer\".</p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "hiv_ds = dataset.loc[dataset[\"indicator_category\"] == \"HIV/AIDS\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p>4.And then we remove empty columns and unnecessary columns</p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "hiv_ds.drop(['indicator_category','bchc_req_meth','source','methods','notes','90pc_con_lvl-low','90pc_con_lvl-high'],\n",
    "                    axis = 1, inplace= True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p>5. Now we remove all the rows which has NaN or NA values</p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "hiv_ds.dropna(axis=0, how='any',inplace= True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "hiv_ds.to_csv(\"data/hiv_aids.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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>indicator</th>\n",
       "      <th>year</th>\n",
       "      <th>sex</th>\n",
       "      <th>race_ethnicity</th>\n",
       "      <th>value</th>\n",
       "      <th>place</th>\n",
       "      <th>95pc_con_lvl-low</th>\n",
       "      <th>95pc_con_lvl-high</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>14189</th>\n",
       "      <td>AIDS Diagnoses Rate (Per 100,000 people)</td>\n",
       "      <td>2010</td>\n",
       "      <td>Both</td>\n",
       "      <td>All</td>\n",
       "      <td>2.3</td>\n",
       "      <td>Fort Worth (Tarrant County), TX</td>\n",
       "      <td>1.6</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14191</th>\n",
       "      <td>AIDS Diagnoses Rate (Per 100,000 people)</td>\n",
       "      <td>2010</td>\n",
       "      <td>Both</td>\n",
       "      <td>All</td>\n",
       "      <td>9.0</td>\n",
       "      <td>Oakland (Alameda County), CA</td>\n",
       "      <td>6.2</td>\n",
       "      <td>12.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14194</th>\n",
       "      <td>AIDS Diagnoses Rate (Per 100,000 people)</td>\n",
       "      <td>2010</td>\n",
       "      <td>Both</td>\n",
       "      <td>All</td>\n",
       "      <td>10.8</td>\n",
       "      <td>Las Vegas (Clark County), NV</td>\n",
       "      <td>9.4</td>\n",
       "      <td>12.3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                      indicator  year   sex race_ethnicity  \\\n",
       "14189  AIDS Diagnoses Rate (Per 100,000 people)  2010  Both            All   \n",
       "14191  AIDS Diagnoses Rate (Per 100,000 people)  2010  Both            All   \n",
       "14194  AIDS Diagnoses Rate (Per 100,000 people)  2010  Both            All   \n",
       "\n",
       "       value                            place  95pc_con_lvl-low  \\\n",
       "14189    2.3  Fort Worth (Tarrant County), TX               1.6   \n",
       "14191    9.0     Oakland (Alameda County), CA               6.2   \n",
       "14194   10.8     Las Vegas (Clark County), NV               9.4   \n",
       "\n",
       "       95pc_con_lvl-high  \n",
       "14189                3.0  \n",
       "14191               12.5  \n",
       "14194               12.3  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hiv_ds.head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2>Analysis</h2>\n",
    "\n",
    "First we'll see how many patients have been reported for cancer in respective years from 2010 to 2016. \n",
    "\n",
    "Following is the process to do the same"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "c_year_2010=hiv_ds[hiv_ds['year']==2010]\n",
    "c_year_2010_count=c_year_2010['year'].count()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(134, 8)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c_year_2010.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "c_year_2011=hiv_ds[hiv_ds['year']==2011]\n",
    "c_year_2011_count=c_year_2011['year'].count()\n",
    "\n",
    "c_year_2012=hiv_ds[hiv_ds['year']==2012]\n",
    "c_year_2012_count=c_year_2012['year'].count()\n",
    "\n",
    "c_year_2013=hiv_ds[hiv_ds['year']==2013]\n",
    "c_year_2013_count=c_year_2013['year'].count()\n",
    "\n",
    "c_year_2014=hiv_ds[hiv_ds['year']==2014]\n",
    "c_year_2014_count=c_year_2014['year'].count()\n",
    "\n",
    "c_year_2015=hiv_ds[hiv_ds['year']==2015]\n",
    "c_year_2015_count=c_year_2015['year'].count()\n",
    "\n",
    "c_year_2016=hiv_ds[hiv_ds['year']==2016]\n",
    "c_year_2016_count=c_year_2016['year'].count()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "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": [
    "fig1 = pd.DataFrame({'Years':['2010', '2011', '2012','2013','2014','2015','2016'], 'Categories':[c_year_2010_count, c_year_2011_count,c_year_2012_count, c_year_2013_count,c_year_2014_count,c_year_2015_count,c_year_2016_count]})\n",
    "ax = fig1.plot.bar(x='Years', rot=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p>Now we calculate the number of cases for each type of cancer. In order to that we will group according to the \n",
    "indicator and take the count.</p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Persons Living with HIV/AIDS Rate (Per 100,000 people)           259\n",
       "HIV Diagnoses Rate (Per 100,000 people)                          221\n",
       "AIDS Diagnoses Rate (Per 100,000 people)                         196\n",
       "HIV-Related Mortality Rate (Age-Adjusted; Per 100,000 people)    127\n",
       "Name: indicator, dtype: int64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sorted_cancer = hiv_ds['indicator'].value_counts()\n",
    "sorted_cancer"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p>And we plot a histogram to see.</p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "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": [
    "labels=list(hiv_ds.columns)\n",
    "sorted_cancer = hiv_ds['indicator'].value_counts().plot(title='Number of cases', kind='bar')\n",
    "plt.show()\n",
    "#label=list(group.columns)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we find out the distribution of cancer patients with respect to the race and ethnicity."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "all=hiv_ds[hiv_ds['race_ethnicity']==\"All\"]\n",
    "all_count=all.race_ethnicity.count()\n",
    "\n",
    "asian=hiv_ds[hiv_ds['race_ethnicity']==\"Asian/PI\"]\n",
    "asian_count=asian.race_ethnicity.count()\n",
    "\n",
    "black=hiv_ds[hiv_ds['race_ethnicity']==\"Black\"]\n",
    "black_count=black.race_ethnicity.count()\n",
    "\n",
    "hispanic=hiv_ds[hiv_ds['race_ethnicity']==\"Hispanic\"]\n",
    "hispanic_count=hispanic.race_ethnicity.count()\n",
    "\n",
    "other=hiv_ds[hiv_ds['race_ethnicity']==\"Other\"]\n",
    "other_count=other.race_ethnicity.count()\n",
    "\n",
    "white=hiv_ds[hiv_ds['race_ethnicity']==\"White\"]\n",
    "white_count=white.race_ethnicity.count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "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": [
    "fig2 = pd.DataFrame({'race_ethnicity':['All', 'Asian/PI', 'Black','Hispanic','Other','White'], 'Count':[all_count, asian_count,black_count, hispanic_count,other_count,white_count]})\n",
    "ax = fig2.plot.bar(x='race_ethnicity', rot=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "hiv_ds=hiv_ds.rename(columns={'95pc_con_lvl-low':'low','95pc_con_lvl-high':'high'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "ds=hiv_ds.drop(['indicator','year','sex','race_ethnicity'\n",
    "                ,'place'],\n",
    "                    axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "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>value</th>\n",
       "      <th>low</th>\n",
       "      <th>high</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>14189</th>\n",
       "      <td>2.3</td>\n",
       "      <td>1.6</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14191</th>\n",
       "      <td>9.0</td>\n",
       "      <td>6.2</td>\n",
       "      <td>12.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14194</th>\n",
       "      <td>10.8</td>\n",
       "      <td>9.4</td>\n",
       "      <td>12.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14197</th>\n",
       "      <td>15.9</td>\n",
       "      <td>13.0</td>\n",
       "      <td>18.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14198</th>\n",
       "      <td>17.6</td>\n",
       "      <td>16.3</td>\n",
       "      <td>18.9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       value   low  high\n",
       "14189    2.3   1.6   3.0\n",
       "14191    9.0   6.2  12.5\n",
       "14194   10.8   9.4  12.3\n",
       "14197   15.9  13.0  18.8\n",
       "14198   17.6  16.3  18.9"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "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": [
    "#very simple plotting\n",
    "fig = plt.figure(1)\n",
    "ax1 = fig.add_subplot(111)\n",
    "ax1.set_xlabel('low')\n",
    "ax1.set_ylabel('value')\n",
    "ax1.set_title('Original Plot')\n",
    "ax1.scatter('low', 'value', data = ds);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Linear Regression with high values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "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": [
    "#very simple plotting\n",
    "fig = plt.figure(1)\n",
    "ax1 = fig.add_subplot(111)\n",
    "ax1.set_xlabel('high')\n",
    "ax1.set_ylabel('value')\n",
    "ax1.set_title('Original Plot')\n",
    "ax1.scatter('high', 'value', data = ds);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "formula: y = [[0.95792542]]x + [-5.71469379]\n"
     ]
    }
   ],
   "source": [
    "x_y = np.array(ds)\n",
    "x, y = x_y[:,0], x_y[:,1]\n",
    "\n",
    "# Reshaping\n",
    "x, y = x.reshape(-1,1), y.reshape(-1, 1)\n",
    "\n",
    "# Linear Regression Object \n",
    "lin_regression = LinearRegression()\n",
    "\n",
    "# Fitting linear model to the data\n",
    "lin_regression.fit(x,y)\n",
    "\n",
    "# Get slope of fitted line\n",
    "m = lin_regression.coef_\n",
    "\n",
    "# Get y-Intercept of the Line\n",
    "b = lin_regression.intercept_\n",
    "\n",
    "# Get Predictions for original x values\n",
    "# you can also get predictions for new data\n",
    "predictions = lin_regression.predict(x)\n",
    "\n",
    "# following slope intercept form \n",
    "print (\"formula: y = {0}x + {1}\".format(m, b) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "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": [
    "plt.scatter(x, y,  color='red')\n",
    "plt.plot(x, predictions, color='blue',linewidth=3)\n",
    "plt.show()"
   ]
  },
  {
   "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": "3.7.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}