injury_violence.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h1><b>Injury and Violence 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": [
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       "    }\n",
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       "</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": [
    "iv_ds = dataset.loc[dataset[\"indicator_category\"] == \"Injury/Violence\"]"
   ]
  },
  {
   "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": [
    "iv_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": [
    "iv_ds.dropna(axis=0, how='any',inplace= True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "iv_ds.to_csv(\"data/injury_violence.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <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>21158</th>\n",
       "      <td>Firearm-Related Emergency Department Visit Rat...</td>\n",
       "      <td>2010</td>\n",
       "      <td>Both</td>\n",
       "      <td>All</td>\n",
       "      <td>1.2</td>\n",
       "      <td>Las Vegas (Clark County), NV</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21161</th>\n",
       "      <td>Firearm-Related Emergency Department Visit Rat...</td>\n",
       "      <td>2010</td>\n",
       "      <td>Both</td>\n",
       "      <td>All</td>\n",
       "      <td>4.7</td>\n",
       "      <td>Chicago, Il</td>\n",
       "      <td>4.5</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21163</th>\n",
       "      <td>Firearm-Related Emergency Department Visit Rat...</td>\n",
       "      <td>2010</td>\n",
       "      <td>Both</td>\n",
       "      <td>Asian/PI</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Las Vegas (Clark County), NV</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
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      "text/plain": [
       "                                               indicator  year   sex  \\\n",
       "21158  Firearm-Related Emergency Department Visit Rat...  2010  Both   \n",
       "21161  Firearm-Related Emergency Department Visit Rat...  2010  Both   \n",
       "21163  Firearm-Related Emergency Department Visit Rat...  2010  Both   \n",
       "\n",
       "      race_ethnicity  value                         place  95pc_con_lvl-low  \\\n",
       "21158            All    1.2  Las Vegas (Clark County), NV               1.0   \n",
       "21161            All    4.7                   Chicago, Il               4.5   \n",
       "21163       Asian/PI    0.2  Las Vegas (Clark County), NV               0.0   \n",
       "\n",
       "       95pc_con_lvl-high  \n",
       "21158                1.3  \n",
       "21161                5.0  \n",
       "21163                0.3  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iv_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=iv_ds[iv_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": [
       "(157, 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=iv_ds[iv_ds['year']==2011]\n",
    "c_year_2011_count=c_year_2011['year'].count()\n",
    "\n",
    "c_year_2012=iv_ds[iv_ds['year']==2012]\n",
    "c_year_2012_count=c_year_2012['year'].count()\n",
    "\n",
    "c_year_2013=iv_ds[iv_ds['year']==2013]\n",
    "c_year_2013_count=c_year_2013['year'].count()\n",
    "\n",
    "c_year_2014=iv_ds[iv_ds['year']==2014]\n",
    "c_year_2014_count=c_year_2014['year'].count()\n",
    "\n",
    "c_year_2015=iv_ds[iv_ds['year']==2015]\n",
    "c_year_2015_count=c_year_2015['year'].count()\n",
    "\n",
    "c_year_2016=iv_ds[iv_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": [
       "Firearm-Related Mortality Rate (Age-Adjusted; Per 100,000 people)                    256\n",
       "Suicide Rate (Age-Adjusted; Per 100,000 people)                                      243\n",
       "Motor Vehicle Mortality Rate (Age-Adjusted; Per 100,000 people)                      239\n",
       "Homicide Rate (Age-Adjusted; Per 100,000 people)                                     222\n",
       "Firearm-Related Emergency Department Visit Rate (Age-Adjusted; Per 10,000 people)     79\n",
       "Name: indicator, dtype: int64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sorted_injury = iv_ds['indicator'].value_counts()\n",
    "sorted_injury"
   ]
  },
  {
   "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(iv_ds.columns)\n",
    "sorted_injury = iv_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=iv_ds[iv_ds['race_ethnicity']==\"All\"]\n",
    "all_count=all.race_ethnicity.count()\n",
    "\n",
    "asian=iv_ds[iv_ds['race_ethnicity']==\"Asian/PI\"]\n",
    "asian_count=asian.race_ethnicity.count()\n",
    "\n",
    "black=iv_ds[iv_ds['race_ethnicity']==\"Black\"]\n",
    "black_count=black.race_ethnicity.count()\n",
    "\n",
    "hispanic=iv_ds[iv_ds['race_ethnicity']==\"Hispanic\"]\n",
    "hispanic_count=hispanic.race_ethnicity.count()\n",
    "\n",
    "other=iv_ds[iv_ds['race_ethnicity']==\"Other\"]\n",
    "other_count=other.race_ethnicity.count()\n",
    "\n",
    "white=iv_ds[iv_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": [
    "iv_ds=iv_ds.rename(columns={'95pc_con_lvl-low':'low','95pc_con_lvl-high':'high'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "ds=iv_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>21158</th>\n",
       "      <td>1.2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21161</th>\n",
       "      <td>4.7</td>\n",
       "      <td>4.5</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21163</th>\n",
       "      <td>0.2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21164</th>\n",
       "      <td>0.3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21166</th>\n",
       "      <td>3.1</td>\n",
       "      <td>2.4</td>\n",
       "      <td>3.8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       value  low  high\n",
       "21158    1.2  1.0   1.3\n",
       "21161    4.7  4.5   5.0\n",
       "21163    0.2  0.0   0.3\n",
       "21164    0.3  0.0   0.6\n",
       "21166    3.1  2.4   3.8"
      ]
     },
     "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": 28,
   "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": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "formula: y = [[0.8238202]]x + [-0.75984772]\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": 30,
   "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
}