progress_report.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "#to reflect changes made in modules\n",
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "\n",
    "#setup\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import sklearn\n",
    "import machine_learning"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Project Introduction\n",
    "The big idea of our project is to  Explore the correlation between gun violence cases with age, location, and/or socioeconomic factors in the area. One main question that we are investigating is has gun reform impacted the number of gun violence cases in the US? We have multiple datasets that show gun-related cases by state and year, firearm laws passed by states, and state populations. The scope of our project is to visualize the number of gun cases in the US using the Gun Violence dataset from the Gun Violence Archive.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Any Changes\n",
    "The original scope of the project (mentioned above) has not changed since the previous proposal. The main focus of the project will be about how the number and types of gun reform laws impacts the number of gun violence cases using the datasets on gun cases, gun reform laws, and state population.\n",
    "\n",
    "\n",
    "- We will **not** identify the correlation between gun violence cases with age and/or socioeconomic factors in each state.\n",
    "- We will **not** assess the degree of severity of specific gun laws on the number of gun violence cases.\n",
    "- We added the state population dataset to assess the number of gun cases with correlation to state population.\n",
    "- We created a decision tree `DecisionTreeClassifier()` and the baseline `MajorityLabelClassifier()` to predict whether the number of gun cases will decrease based on the types of gun reform laws and total laws."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Reading"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Reading in data for gun violence counts and firearm laws\n",
    "gun_data = pd.read_csv('gun-violence-data_01-2013_03-2018.csv')\n",
    "missing_row = ['sban_1', '2017-10-01', 'Nevada', 'Las Vegas', 'Mandalay Bay 3950 Blvd S', 59, 489, 'https://en.wikipedia.org/wiki/2017_Las_Vegas_shooting', 'https://en.wikipedia.org/wiki/2017_Las_Vegas_shooting', '-', '-', '-', '-', '-', '36.095', 'Hotel', \n",
    "               '-115.171667', 47, 'Route 91 Harvest Festiva; concert, open fire from 32nd floor. 47 guns seized; TOTAL:59 kill, 489 inj, number shot TBD,girlfriend Marilou Danley POI', '-', '-', '-', '-', '-', '-', '-', '-', '-', '-']\n",
    "gun_data.loc[len(gun_data)] = missing_row\n",
    "\n",
    "gunlaws = pd.read_csv('statefirearmlaws.csv')\n",
    "gun_laws = pd.DataFrame(data = gunlaws,columns=['state', 'year', 'lawtotal'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Cleaning\n",
    "Show clearly how you cleaned your data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Exploratory Data Analysis\n",
    "The datasets that we are using contain information about gun violence incidents from 2013-2018, gun reform laws from 1991-2020, and state populations from 2010-2019. The Gun Violence dataset indicates that there has been an increase in gun violence from 2014 to 2017. Additionally, there has been an overall increase in gun reform laws across the US, which led us to explore whether gun reform laws have an impact on gun violence at the state level."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='year', ylabel='counts'>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "gun_data['date'] = pd.to_datetime(gun_data['date'])\n",
    "gun_data['year'] = gun_data['date'].dt.year\n",
    "gun_data_year = gun_data\n",
    "gun_data_year = gun_data_year.groupby(['year']).size().reset_index(name='counts')\n",
    "\n",
    "sns.set(rc = {'figure.figsize':(10,8)})\n",
    "sns.barplot(x=\"year\", y=\"counts\", data=gun_data_year.loc[1:4, :], palette='viridis')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='year', ylabel='lawtotal'>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "gun_laws_year = gun_laws.groupby(['year']).sum().reset_index()\n",
    "sns.set(rc = {'figure.figsize':(15,8)})\n",
    "sns.barplot(x='year', y='lawtotal', data=gun_laws_year, palette='viridis')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# At least one visualization that tests an interesting hypothesis\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The hypothesis that we will be testing is that states with more strict gun laws have a smaller amount of gun violence incidents compared to states with less strict gun laws."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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>state</th>\n",
       "      <th>counts</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Illinois</td>\n",
       "      <td>17556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>California</td>\n",
       "      <td>16306</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Florida</td>\n",
       "      <td>15029</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>Texas</td>\n",
       "      <td>13577</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>Ohio</td>\n",
       "      <td>10244</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         state  counts\n",
       "13    Illinois   17556\n",
       "4   California   16306\n",
       "9      Florida   15029\n",
       "43       Texas   13577\n",
       "35        Ohio   10244"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_state = gun_data.groupby(['state']).size().reset_index(name='counts')\n",
    "df_state = df_state.sort_values(by='counts', ascending=False)\n",
    "sns.set(rc = {'figure.figsize':(10,8)})\n",
    "sns.barplot(x='counts', y='state', data=df_state, palette='viridis').set(title=\"Gun Violence Count by State\");\n",
    "\n",
    "df_state.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "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>state</th>\n",
       "      <th>year</th>\n",
       "      <th>lawtotal</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>California</td>\n",
       "      <td>2020</td>\n",
       "      <td>111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>California</td>\n",
       "      <td>2019</td>\n",
       "      <td>111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>California</td>\n",
       "      <td>2018</td>\n",
       "      <td>109</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146</th>\n",
       "      <td>California</td>\n",
       "      <td>2017</td>\n",
       "      <td>106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145</th>\n",
       "      <td>California</td>\n",
       "      <td>2016</td>\n",
       "      <td>104</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>714</th>\n",
       "      <td>Mississippi</td>\n",
       "      <td>2015</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>747</th>\n",
       "      <td>Missouri</td>\n",
       "      <td>2018</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>359</th>\n",
       "      <td>Idaho</td>\n",
       "      <td>2020</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>358</th>\n",
       "      <td>Idaho</td>\n",
       "      <td>2019</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>357</th>\n",
       "      <td>Idaho</td>\n",
       "      <td>2018</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1500 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           state  year  lawtotal\n",
       "149   California  2020       111\n",
       "148   California  2019       111\n",
       "147   California  2018       109\n",
       "146   California  2017       106\n",
       "145   California  2016       104\n",
       "..           ...   ...       ...\n",
       "714  Mississippi  2015         2\n",
       "747     Missouri  2018         2\n",
       "359        Idaho  2020         1\n",
       "358        Idaho  2019         1\n",
       "357        Idaho  2018         1\n",
       "\n",
       "[1500 rows x 3 columns]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_laws = gun_laws.sort_values(by='lawtotal', ascending=False)\n",
    "sns.set(rc = {'figure.figsize':(10,8)})\n",
    "sns.barplot(x='lawtotal', y='state', data=df_laws, palette='viridis').set(title=\"Gun Laws by State\");\n",
    "\n",
    "df_laws"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Looking at the visualizations, Illinois has the highest gun violence incidents and Hawaii has the least number of incidents. However, the population difference is very large. Because of this, we will calculate a proportion that represents (# of cases/ population). \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                  state      2013      2014      2015      2016      2017  \\\n",
      "8  District of Columbia  650581.0  662328.0  675400.0  685815.0  694906.0   \n",
      "1                Alaska  737068.0  736283.0  737498.0  741456.0  739700.0   \n",
      "\n",
      "       2018  prop_cases  \n",
      "8  701547.0    0.004554  \n",
      "1  735139.0    0.001835  \n",
      "      state       2013       2014       2015       2016       2017       2018  \\\n",
      "2   Arizona  6632764.0  6730413.0  6829676.0  6941072.0  7044008.0  7158024.0   \n",
      "11   Hawaii  1408243.0  1414538.0  1422052.0  1427559.0  1424393.0  1420593.0   \n",
      "\n",
      "    prop_cases  \n",
      "2     0.000325  \n",
      "11    0.000203  \n"
     ]
    }
   ],
   "source": [
    "uspopulation = pd.read_csv('us_states_census.csv') \n",
    "population = pd.DataFrame(data=uspopulation, columns=['state','2013','2014','2015','2016','2017', '2018'])\n",
    "\n",
    "state_cases = gun_data.groupby(['state']).size().reset_index(name='counts')\n",
    "state_cases = state_cases.sort_values(by='state', ascending=True)\n",
    "state_cases['population'] = population['2018']\n",
    "\n",
    "prop_cases = population\n",
    "prop_cases['prop_cases'] = state_cases['counts'] / population['2018']\n",
    "prop_cases = prop_cases.sort_values(by=['prop_cases'], ascending=False)\n",
    "print(prop_cases.head(2))\n",
    "print(prop_cases.tail(2))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Looking at the new proportion calculations, we will compare the proportion of cases between **Alaska** and **Hawaii** and if the number of laws affects the proportion.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0.5, 1.0, 'Proportion of cases by population in 2018'),\n",
       " Text(0, 0.5, 'Proportion of cases per population'),\n",
       " Text(0.5, 0, 'State')]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "top_state_inc = pd.DataFrame(population[population['state'] == 'Alaska'])\n",
    "low_state_inc = pd.DataFrame(population[population['state'] =='Hawaii'])\n",
    "\n",
    "list_states_inc = [top_state_inc, low_state_inc]\n",
    "\n",
    "stats_inc = pd.concat(list_states_inc)\n",
    "stats_inc\n",
    "sns.set(rc = {'figure.figsize':(6,4)})\n",
    "\n",
    "ax = sns.barplot(data=stats_inc, x=\"state\", y=\"prop_cases\") #.set(title=\"Proportion of cases by population in 2018\")\n",
    "ax.set(title = \"Proportion of cases by population in 2018\",\n",
    "        ylabel = \"Proportion of cases per population\",\n",
    "        xlabel=\"State\")\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In Alaska, roughly 3 laws were passed each year between 2013 and 2018, and the proportion of cases by population is 0.001835. In Hawaii, roughly 79-80 laws were added by 2018. There is a lower proportion, 0.000203. \n",
    "\n",
    "# Hypothesis Results\n",
    "\n",
    "Our hypothesis is correct. Alaska has one of the  highest proportions of gun-related incidents by population, but a lower number of gun laws. Hawaii has a lower proportion of gun-related incidents by population, and a higher number of gun laws passes. Given that our hypothesis was that states with low gun-related incidents have higher gun laws, and vice versa, our hypothesis was correct. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "stat=-0.183, p=0.204\n",
      "There is not enough evidence to reject H0. Probably independent\n"
     ]
    }
   ],
   "source": [
    "import scipy\n",
    "df_states = pd.read_csv('df_states.csv')\n",
    "df_states.head()\n",
    "stat, p = scipy.stats.pearsonr(df_states['per_counts'], df_states['lawtotal'])\n",
    "print('stat=%.3f, p=%.3f' % (stat, p))\n",
    "if p > 0.05:\n",
    "\tprint('There is not enough evidence to reject H0. Probably independent')\n",
    "else:\n",
    "\tprint('There is enough evidence to reject H0. Probably dependent')\n",
    "# law_tatoal vs. gun_violence_counts per 10000 people"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set_style('white')\n",
    "ax = sns.lmplot(x=\"per_counts\", y=\"lawtotal\", data=df_states, scatter_kws={'s': 6}, legend=True)\n",
    "ax.set(ylabel=\"Gun Laws\")\n",
    "ax.set(xlabel=\"Gun Violence per 10,000 people\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# ML Analysis: Decision Tree\n",
    "on your dataset, along with a baseline comparison and an interpretation of the result that you obtain. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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>state</th>\n",
       "      <th>year</th>\n",
       "      <th>proportion_harmed</th>\n",
       "      <th>decrease_in_gun_violence</th>\n",
       "      <th>felony</th>\n",
       "      <th>invcommitment</th>\n",
       "      <th>invoutpatient</th>\n",
       "      <th>danger</th>\n",
       "      <th>drugmisdemeanor</th>\n",
       "      <th>alctreatment</th>\n",
       "      <th>...</th>\n",
       "      <th>expartedating</th>\n",
       "      <th>dvrosurrender</th>\n",
       "      <th>dvrosurrendernoconditions</th>\n",
       "      <th>dvrosurrenderdating</th>\n",
       "      <th>expartesurrender</th>\n",
       "      <th>expartesurrendernoconditions</th>\n",
       "      <th>expartesurrenderdating</th>\n",
       "      <th>dvroremoval</th>\n",
       "      <th>stalking</th>\n",
       "      <th>lawtotal</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Alabama</td>\n",
       "      <td>2015</td>\n",
       "      <td>0.000195</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Alabama</td>\n",
       "      <td>2016</td>\n",
       "      <td>0.000257</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Alabama</td>\n",
       "      <td>2017</td>\n",
       "      <td>0.000287</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Alaska</td>\n",
       "      <td>2015</td>\n",
       "      <td>0.000209</td>\n",
       "      <td>False</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Alaska</td>\n",
       "      <td>2016</td>\n",
       "      <td>0.000258</td>\n",
       "      <td>False</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 139 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     state  year  proportion_harmed  decrease_in_gun_violence  felony  \\\n",
       "0  Alabama  2015           0.000195                     False       0   \n",
       "1  Alabama  2016           0.000257                     False       0   \n",
       "2  Alabama  2017           0.000287                     False       0   \n",
       "3   Alaska  2015           0.000209                     False       1   \n",
       "4   Alaska  2016           0.000258                     False       1   \n",
       "\n",
       "   invcommitment  invoutpatient  danger  drugmisdemeanor  alctreatment  ...  \\\n",
       "0              1              0       1                0             0  ...   \n",
       "1              1              0       1                0             0  ...   \n",
       "2              1              0       1                0             0  ...   \n",
       "3              0              0       0                0             0  ...   \n",
       "4              0              0       0                0             0  ...   \n",
       "\n",
       "   expartedating  dvrosurrender  dvrosurrendernoconditions  \\\n",
       "0              0              0                          0   \n",
       "1              0              0                          0   \n",
       "2              0              0                          0   \n",
       "3              0              0                          0   \n",
       "4              0              0                          0   \n",
       "\n",
       "   dvrosurrenderdating  expartesurrender  expartesurrendernoconditions  \\\n",
       "0                    0                 0                             0   \n",
       "1                    0                 0                             0   \n",
       "2                    0                 0                             0   \n",
       "3                    0                 0                             0   \n",
       "4                    0                 0                             0   \n",
       "\n",
       "   expartesurrenderdating  dvroremoval  stalking  lawtotal  \n",
       "0                       0            0         0        10  \n",
       "1                       0            0         0        10  \n",
       "2                       0            0         0        10  \n",
       "3                       0            0         0         3  \n",
       "4                       0            0         0         3  \n",
       "\n",
       "[5 rows x 139 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#clean and merge data set for ML \n",
    "gun_cases = 'gun-violence-data_01-2013_03-2018.csv'\n",
    "gun_laws = 'statefirearmlaws.csv'\n",
    "population = 'us_states_census.csv'\n",
    "data = machine_learning.combineDatasets(gun_cases, gun_laws, population)\n",
    "data.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "#create features and labels for ML application. Label is (0,1) based on \n",
    "#whether a decrease in gun_violence was observed\n",
    "X, y = machine_learning.create_features_labels(data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### More about the target variable: `decrease_in_gun_violence`\n",
    "\n",
    "`decrease_in_gun_violence` is measured by comparing the proportion of people harmed (killed or injured) \n",
    "for a state's current and previous years from the time frame of 2014-2017. Thus, each state in our data set `data` has an entry for 2015, 2016, and 2017. \n",
    "\n",
    "Using Alabama as an example of how we determine the target variable for each state+year, there are three entries in our dataset: Alabama 2015, Alabama 2016, and Alabama 2017. \n",
    "Alabama 2015's value for `decrease_in_gun_violence` is determined by looking at the proportion of people harmed in the previous year, Alabama 2014, and the proportion of people harmed in the current row's year, Alabama 2015. If there is a decrease in proportion of people harmed then `decrease_in_gun_violence=True`. Otherwise `decrease_in_gun_violence=False`. The same process is repeated to get target variable values for Alabama 2016 and Alabama 2017. \n",
    "\n",
    "We calculate the proportion of people harmed as a raw count divided by the population for that state+year to account for increases/decreases in population affecting the rate of gun violence in a state at a given year. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of examples in our entire dataset where decrease_in_gun_violence=0: 101\n",
      "Number of examples in our entire dataset where decrease_in_gun_violence=1: 49\n"
     ]
    }
   ],
   "source": [
    "#the proportion of classes w.r.t 'decrease_in_gun_violence' (0,1) is unbalanced\n",
    "print(f'Number of examples in our entire dataset where decrease_in_gun_violence=0: {len(y[y == 0])}')\n",
    "print(f'Number of examples in our entire dataset where decrease_in_gun_violence=1: {len(y[y == 1])}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.6838235294117647\n"
     ]
    }
   ],
   "source": [
    "#building our baseline classifier (predicts the majority label)\n",
    "baseline = machine_learning.MajorityLabelClassifier()\n",
    "baseline.fit(X, y)\n",
    "labels = baseline.predict(X)\n",
    "print(baseline.evaluate_accuracy(y, labels))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Accuracy: 0.8866666666666667\n",
      "Test Accuracy: 0.43333333333333335\n",
      "precision: [0.73333333 0.13333333]\n",
      "recall   : [0.45833333 0.33333333]\n",
      "f1 score : [0.56410256 0.19047619]\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#evaluating our Decision Tree classifier by training, fitting, and predicting on our data\n",
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2,random_state=1) #changed split to 80% train, 20% test\n",
    "\n",
    "decision_tree = machine_learning.DecisionTree()\n",
    "decision_tree.fit(X_train, y_train)\n",
    "y_pred = decision_tree.predict(X_test)\n",
    "\n",
    "decision_tree.display_metrics(X, y, y_test, y_pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Reflection\n",
    "`A discussion of the following:`\n",
    "- `What is the hardest part of the project that you’ve encountered so far?`\n",
    "- `What are your initial insights?`\n",
    "- `Are there any concrete results you can show at this point? If not, why not?`\n",
    "- `Going forward, what are the current biggest problems you’re facing?`\n",
    "- `Do you think you are on track with your project? If not, what parts do you need to dedicate more time to?`\n",
    "- `Given your initial exploration of the data, is it worth proceeding with your project, why? If not, how are you going to change your project and why do you think it’s better than your current results?`\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The hardest part of the project that we've encountered so far would be trying to incorporate a robust machine learning model with our dataset. Coming up with the features that would yield a good prediction as to whether gun violence would decrease with information about proportion of individuals harmed, as well as gun reform information, turns out to be quite difficult on its own. \n",
    "\n",
    "Our initial insights from this project at this stage mainly center around the idea that predicting trends in gun violence is a complex problem in itself, due to various socioeconomic factors, as well as gun reform implemented. Gun violence also tends to be motivated by a myriad of different factors at the individual suspect level, so attributing what causes gun violence is incredibly difficult to predict to an exact degree.\n",
    "\n",
    "At this point, there are no concrete results we can show, as our model seems to yield inconclusive results on whether we can predict trends in gun violence rates from gun reform data. This \n",
    "inconclusivity can be due to our imbalanced dataset, which is biased in the sense that there are more instances of cases where gun violence did NOT decrease than the opposite. Going forward, our team will need to decide how to address this issue, as well as what other possible features we can create/extract from our data to try and increase our model's predictive performance. \n",
    "\n",
    "At this stage, our team is on track with our project. The next stage would place a heavier emphasis on tuning our model by incorporating cross validation, tree pruning, and additional features to try and improve prediction performance on test data. Our team speculates that incorporating all of the aforementioned factors would help our decision tree generalize better to unseen test data, and avoid the current scenario of our decision tree classifier overfitting on our train data. The cross validation has the potential to help us tune hyperparameters that go into building our decision tree, which can increase the performance of our classifier. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Next Steps\n",
    "`What you plan to accomplish in the next month and how you plan to evaluate whether your project achieved the goals you set for it.`\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the next month, our team plans to implement the following methods to achieve a better accuracy, precision, recall, and f1 score for our decision tree: \n",
    "\n",
    "- using cross validation to tune decision tree hyperparameters\n",
    "- pruning the tree to try and avoid overfitting\n",
    "- handle class imbalances in data\n",
    "- add additional features about participant info such as mean participant age, mean victim age, mean suspect age on a per state/year basis\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Deliverables: a PDF of your jupyter notebook, with a link to the notebook located in your repository; Notebook can’t be more than 5 pages"
   ]
  }
 ],
 "metadata": {
  "interpreter": {
   "hash": "87eb1506ace0c76867aaab3383ec0484b429857f88291368ffd3c4acf06de760"
  },
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}