life_death_expectancy.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h1><b>Life and Death Expectancy 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 analysis</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": [
    "lde_ds = dataset.loc[dataset[\"indicator_category\"] == \"Life Expectancy and Death Rate (Overall)\"]"
   ]
  },
  {
   "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": [
    "lde_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": [
    "lde_ds.dropna(axis=0, how='any',inplace= True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "lde_ds.to_csv(\"data/life_death.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>24934</th>\n",
       "      <td>All-Cause Mortality Rate (Age-Adjusted; Per 10...</td>\n",
       "      <td>2010</td>\n",
       "      <td>Both</td>\n",
       "      <td>All</td>\n",
       "      <td>583.3</td>\n",
       "      <td>San Francisco, CA</td>\n",
       "      <td>574.3</td>\n",
       "      <td>592.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24935</th>\n",
       "      <td>All-Cause Mortality Rate (Age-Adjusted; Per 10...</td>\n",
       "      <td>2010</td>\n",
       "      <td>Both</td>\n",
       "      <td>All</td>\n",
       "      <td>606.0</td>\n",
       "      <td>Seattle, WA</td>\n",
       "      <td>586.2</td>\n",
       "      <td>626.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24936</th>\n",
       "      <td>All-Cause Mortality Rate (Age-Adjusted; Per 10...</td>\n",
       "      <td>2010</td>\n",
       "      <td>Both</td>\n",
       "      <td>All</td>\n",
       "      <td>630.1</td>\n",
       "      <td>San Diego County, CA</td>\n",
       "      <td>621.1</td>\n",
       "      <td>639.1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                               indicator  year   sex  \\\n",
       "24934  All-Cause Mortality Rate (Age-Adjusted; Per 10...  2010  Both   \n",
       "24935  All-Cause Mortality Rate (Age-Adjusted; Per 10...  2010  Both   \n",
       "24936  All-Cause Mortality Rate (Age-Adjusted; Per 10...  2010  Both   \n",
       "\n",
       "      race_ethnicity  value                 place  95pc_con_lvl-low  \\\n",
       "24934            All  583.3     San Francisco, CA             574.3   \n",
       "24935            All  606.0           Seattle, WA             586.2   \n",
       "24936            All  630.1  San Diego County, CA             621.1   \n",
       "\n",
       "       95pc_con_lvl-high  \n",
       "24934              592.4  \n",
       "24935              626.3  \n",
       "24936              639.1  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lde_ds.head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h1>Analysis</h1>\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=lde_ds[lde_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": [
       "(83, 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=lde_ds[lde_ds['year']==2011]\n",
    "c_year_2011_count=c_year_2011['year'].count()\n",
    "\n",
    "c_year_2012=lde_ds[lde_ds['year']==2012]\n",
    "c_year_2012_count=c_year_2012['year'].count()\n",
    "\n",
    "c_year_2013=lde_ds[lde_ds['year']==2013]\n",
    "c_year_2013_count=c_year_2013['year'].count()\n",
    "\n",
    "c_year_2014=lde_ds[lde_ds['year']==2014]\n",
    "c_year_2014_count=c_year_2014['year'].count()\n",
    "\n",
    "c_year_2015=lde_ds[lde_ds['year']==2015]\n",
    "c_year_2015_count=c_year_2015['year'].count()\n",
    "\n",
    "c_year_2016=lde_ds[lde_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": [
       "All-Cause Mortality Rate (Age-Adjusted; Per 100,000 people)    420\n",
       "Life Expectancy at Birth (Years)                               190\n",
       "Name: indicator, dtype: int64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sorted_lde = lde_ds['indicator'].value_counts()\n",
    "sorted_lde"
   ]
  },
  {
   "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(lde_ds.columns)\n",
    "sorted_lde = lde_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=lde_ds[lde_ds['race_ethnicity']==\"All\"]\n",
    "all_count=all.race_ethnicity.count()\n",
    "\n",
    "asian=lde_ds[lde_ds['race_ethnicity']==\"Asian/PI\"]\n",
    "asian_count=asian.race_ethnicity.count()\n",
    "\n",
    "black=lde_ds[lde_ds['race_ethnicity']==\"Black\"]\n",
    "black_count=black.race_ethnicity.count()\n",
    "\n",
    "hispanic=lde_ds[lde_ds['race_ethnicity']==\"Hispanic\"]\n",
    "hispanic_count=hispanic.race_ethnicity.count()\n",
    "\n",
    "other=lde_ds[lde_ds['race_ethnicity']==\"Other\"]\n",
    "other_count=other.race_ethnicity.count()\n",
    "\n",
    "white=lde_ds[lde_ds['race_ethnicity']==\"White\"]\n",
    "white_count=white.race_ethnicity.count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "scrolled": true
   },
   "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": [
    "lde_ds=lde_ds.rename(columns={'95pc_con_lvl-low':'low','95pc_con_lvl-high':'high'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "x='95pc_con_lvl-low'\n",
    "y='95pc_con_lvl-high'\n",
    "ds=lde_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>24934</th>\n",
       "      <td>583.3</td>\n",
       "      <td>574.3</td>\n",
       "      <td>592.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24935</th>\n",
       "      <td>606.0</td>\n",
       "      <td>586.2</td>\n",
       "      <td>626.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24936</th>\n",
       "      <td>630.1</td>\n",
       "      <td>621.1</td>\n",
       "      <td>639.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24937</th>\n",
       "      <td>673.5</td>\n",
       "      <td>647.4</td>\n",
       "      <td>699.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24939</th>\n",
       "      <td>751.0</td>\n",
       "      <td>736.9</td>\n",
       "      <td>765.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       value    low   high\n",
       "24934  583.3  574.3  592.4\n",
       "24935  606.0  586.2  626.3\n",
       "24936  630.1  621.1  639.1\n",
       "24937  673.5  647.4  699.6\n",
       "24939  751.0  736.9  765.0"
      ]
     },
     "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": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "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 = [[0.94706288]]x + [-6.94855848]\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
}