DS3000_FP4_Section01_Group10.ipynb

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   "source": [
    "<center> <h2> DS 3000 - Fall 2019</h2> </center>\n",
    "<center> <h3> DS Report </h3> </center>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<center> <h1>Analyzing the performance of the MBTA</h1> </center>\n",
    "<center><h4>Lei, Dan, and Ethan</h4></center>\n"
   ]
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    "  "
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    "#### Executive Summary:\n",
    "\n",
    "This project is an analysis of the MBTA, specifically the subways, and what conditions can negatively affect the rider's satisfaction with the MBTA and their willingness to continue using public transport. \n",
    "\n",
    "The data was acquired from mbtabackontrack, and was cleaned by renaming various columns, removing extraneous columns and filtering for subways.\n"
   ]
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    "## Outline\n",
    "1. <a href='#1'>INTRODUCTION</a>\n",
    "2. <a href='#2'>METHOD</a>\n",
    "3. <a href='#3'>RESULTS</a>\n",
    "4. <a href='#4'>DISCUSSION</a>"
   ]
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   "source": [
    "## 1. INTRODUCTION (Ethan)"
   ]
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  {
   "cell_type": "markdown",
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   "source": [
    "In this section, orient your readers to your project. You've already written some of these in previous deliverables. Based on your final analysis, revise your problem statement and write a concise introduction section. This section should touch upon the following points, but should be written in full paragraphs. Your writing should incorporate all of these points (and more if you like) in a coherent way. Remember that you are trying to convince your readers that this is an important problem to tackle. \n",
    "\n",
    "Problem Statement\n",
    "* Describe the problem you would like to tackle. \n",
    "* What is the topic of your project? \n",
    "* What do you want to learn about it?\n",
    "\n",
    "Significance of the Problem\n",
    "* Why is it important to tackle this problem in your project?\n",
    "* In what ways could the insights from this project be useful?\n",
    "* Has there been previous work on your topic? Do some research into your topic. Cite your sources appropriately. You can use the numbered reference format or APA (if you are more comfortable with it).\n",
    "\n",
    "Questions/Hypothesis\n",
    "* End this section with a list of questions and hypotheses\n",
    "* You should tie these questions/hypotheses to the problem statement and its significance\n",
    "    * e.g. Given the aforementioned problem and its importance, we set out to tackle the following questions:\n",
    "    \n",
    "**Requirement:**\n",
    "* You should have at least one question tapping into the comparison of various machine learning algorithms in predicting your target variable from your features variables."
   ]
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    "The MBTA is a very old public transport system, and as a result of that age, some cracks have begun to show. Public transit is a very important service for many commuters who do not own cars, and because of that, improving public transit can make a direct improvement on many people’s lives. For our project, we want to take a look at what exactly the flaws are in the train system, and which ones are commuters most sensitive to. Since our datasets include information about wait times, ridership count, rider satisfaction, and many specifics about the train lines themselves, we will do this by examining how these factors affect the overall satisfaction with the MBTA, and how different train lines vary in their quality.\n",
    "\n",
    "\n",
    "The questions we have formulated regarding are dataset are as follows:\n",
    "* Which trains have a significant difference in average arrival delays?\n",
    "* How does customer satisfaction vary with the number of riders, and type of transport? (and assuming it is affected by those, What could we suggest for the MBTA to do to increase satisfaction?)\n",
    "* How does the MBTA fare increase affect customer satisfaction and ridership?\n",
    "\n",
    "The hypotheses we have formulated regarding are dataset are as follows:\n",
    "* Long arrival delays negatively affect customer satisfaction.\n",
    "* Congestion negatively affect customer satisfaction.\n",
    "* Increased MBTA fare lowers the ridership.\n",
    "\n",
    "In the below sections, we will be taking a look at the relationships between these factors and come to a conclusion on which have a larger effect on rider's satisfaction and which have less.\n"
   ]
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    "<a id=\"2\"></a>\n",
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   "source": [
    "## 2. METHOD"
   ]
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   "source": [
    "### 2.1. Data Acquisition (Ethan)\n",
    "\n",
    "* Describe where you obtained your data. Provide a link to the original source. \n",
    "* If you scraped your data, include your code as a script file.\n",
    "* Your data should be stored in an online repository (e.g., GitHub) and your code should retrieve your data from that online resource. You can read csv files from the Web in the same way that you read files from local drive.\n",
    "* Describe the dataset and variables. What do variables represent?\n"
   ]
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   "cell_type": "markdown",
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   "source": [
    "All the data for project comes from this website: https://mbtabackontrack.com/performance/#/download. It contains a large amount of data concerning the MBTA that is updated on a monthly basis.\n",
    "#### Weekly Ridership\n",
    "This dataset holds information on how many riders took the MBTA on a given week.\n",
    "\n",
    "**Variable name in file**|**Description**\n",
    ":-----:|:-----:\n",
    "Date|Date for which month of the data recorded\n",
    "Mode|The type of the MBTA\n",
    "Ridership|Number of trips of passengers in the MBTA system for the specific period\n",
    "CountOfDates|The number of weekdays in this month\n",
    "Avg\\_Ridership|Average Ridership per weekday\n",
    "Route of Line|Specific line of the MBTA\n",
    "\n",
    "#### Ontime Performance\n",
    "This dataset provides information about how reliable a commuter service was for a given week.\n",
    "\n",
    "**Variable Name in File**|**Description**\n",
    ":-----:|:-----:\n",
    "gtfs\\_route\\_id|The id for the transport.\n",
    "gtfs\\_route\\_short\\_name|The short name for the route\n",
    "gtfs\\_route\\_long\\_name|The full name of the route: (Red Line, Green Line, Framingham/Worcester Line, etc)\n",
    "gtfs\\_route\\_desc|The type of transport: (Bus, Subway, Commuter Rail). Also differentiates between local buses, express buses, and key bus routes.\n",
    "route\\_category|Either displays the name of the subway line, if the bus is key or not, or nothing in the case of commuter rails.\n",
    "mode\\_type|The type of transport: The type of transport: (Bus, Subway, Commuter Rail). Does not differentiate between types of buses.\n",
    "service\\_date|The day that the reliability was measured.\n",
    "peak\\_offpeak\\_ind|Whether the service was peak or off-peak.\n",
    "metric\\_type|The metric which reliability was measured for the transport type. Each have slightly different ways of measuring reliability.\n",
    "otp\\_numerator|The numerator of the On-Time Percentage.\n",
    "otp\\_denominator|The denominator of the On-Time Percentage.\n",
    "\n",
    "#### Rider Satisfaction\n",
    "This dataset provides data on a monthly survey conducted by the MBTA, gauging the satisfaction of the riders of MBTA regarding specific and general parts of the MBTA.\n",
    "\n",
    "**Variable Name in File**|**Description**\n",
    ":-----:|:-----:\n",
    "survey_id|The id of the row\n",
    "survey_date|The date that the survey was conducted (Usually the first day of the month)\n",
    "survey_name|The name of the survey\n",
    "question_description|The question being asked\n",
    "response_total|The amount of responses received\n",
    "response_x_text|The text of response for a multiple-choice answer\n",
    "response_x_percent|The percent of participants that chose this response"
   ]
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   "source": [
    "### 2.2. Variables (Lei / Dan)\n",
    "* If you are testing hypotheses, what are your IVs and DVs?\n",
    "* For your predictive models, what are your features and target variables?\n",
    "\n",
    "For the first hypothesis, IV is long arrival delays which is represented with reliability of the trains as well as DV is customer satisfaction which is represented as the score from wait time at stop and overall rating of MBTA. Then in the second hypothesis, IV is cogestion which is showed as ridership of the trains. DV keeps the same as customer satisfaction but it's represented with vehicle crowdedness from questionnaire from customer surveys. For the last hypothesis we have, IV is the increased MBTA fare and the DV is ridership."
   ]
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   "source": [
    "### 2.3. Data Analysis (Dan)\n",
    "* Specifically describe your predictive model. What outcome variable are you going to predict from what feature variables?\n",
    "* Describe whether this is a supervised or unsupervised learning problem. Also identify the sub-category of the learning task (e.g. classification).\n",
    "* What machine learning algorithms are you going to use? Why?"
   ]
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    "<a id=\"3\"></a>\n",
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   "source": [
    "## 3. RESULTS"
   ]
  },
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   "source": [
    "### 3.1. Data Wrangling (Ethan)\n",
    "* Perform simple data cleaning (delete extra columns, deal with NA values, etc.)\n",
    "* Perform data wrangling to extract your features and target values (e.g., grouping your dataframe by columns, applying functions to format dataframes, etc.)\n",
    "* Preprocess your variables (e.g., scaling/transforming feature variables to normalize them)\n",
    "* Feature extraction (dummy variables, new features from existing features, etc.)\n",
    "* Use one feature selection technique to select a subset of your original features\n"
   ]
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  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Returns a DataFrame with statistics about the reliability of a transport service.\n",
    "def get_reliability_df():\n",
    "    df = pd.read_csv(\"https://raw.githubusercontent.com/ethan-leba/data-science-mtba/master/resources/combined_ontime_performance.csv\")\n",
    "    \n",
    "    # Filter out rows not concerning the trains as we are not analyzing this data\n",
    "    df = df[df['mode_type'] == 'Rail']\n",
    "    \n",
    "    # Create a reliability percentage row\n",
    "    df['pct_reliability'] = df['otp_numerator'] / df['otp_denominator']\n",
    "    \n",
    "    # Remove extraneous columns\n",
    "    df = df[['gtfs_route_id', 'service_date', 'pct_reliability']]\n",
    "    \n",
    "    # Rename columns\n",
    "    df = df.rename(columns={'gtfs_route_id':'train_line', 'service_date':'date'})\n",
    "    \n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Returns a DataFrame with statistics about the rider's satisfaction with the MBTA.\n",
    "def get_rider_satisfaction_df():\n",
    "    # The CSV file contains 7 categories of satisfaction, ranging from\n",
    "    # Extremely dissatisfied to extremely satisfied, and a percentage of \n",
    "    # responses for each answer. To make analysis easier to conduct, we are\n",
    "    # converting these columns in a score ranging from 1 to 7 of satisfaction.\n",
    "    def response_to_num(df, amt):\n",
    "        pct = df.copy()[\"response_1_percent\"]\n",
    "        for x in range(2,amt):\n",
    "            pct += df[f\"response_{x}_percent\"] * x \n",
    "        return pct\n",
    "    \n",
    "    df = pd.read_csv(\"https://raw.githubusercontent.com/ethan-leba/data-science-mtba/master/resources/combined_rider_satisfaction.csv\")\n",
    "    \n",
    "    # Drop rows with N/A values\n",
    "    df = df.dropna()\n",
    "    \n",
    "    # Sets the percentage row\n",
    "    df['satisfaction_score'] = response_to_num(df, 7)\n",
    "    \n",
    "    # Remove extraneous columns\n",
    "    df = df[['survey_date', 'question_description', 'satisfaction_score']]\n",
    "    \n",
    "    df = df.rename(columns={\"survey_date\":\"date\"})\n",
    "    \n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Returns a DataFrame with statistics about the amount of ridership a public transport service recieves.\n",
    "def get_weekly_ridership_df():\n",
    "    df = pd.read_csv(\"https://raw.githubusercontent.com/ethan-leba/data-science-mtba/master/resources/combined_weekly_ridership.csv\")\n",
    "    \n",
    "    # We are only looking at trains\n",
    "    df = df[df['MODE'] == 'Rail']\n",
    "    \n",
    "    # Remove extraneous columns\n",
    "    df = df[[\"date\", \"RIDERSHIP\", \"AVG_RIDERSHIP\", \"ROUTE_OR_LINE\"]]\n",
    "    \n",
    "    # Rename columns\n",
    "    df = df.rename(columns={\"RIDERSHIP\": 'ridership', \"AVG_RIDERSHIP\": 'avg_ridership', 'ROUTE_OR_LINE':'train_line'})\n",
    "    \n",
    "    # Formatting (removing 'Line')\n",
    "    df['train_line'] = df['train_line'].apply(lambda x: x[0:len(x) - 5])\n",
    "    \n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "reliability_df = get_reliability_df()\n",
    "satisfaction_df = get_rider_satisfaction_df()\n",
    "ridership_df = get_weekly_ridership_df()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [],
   "source": [
    "rel = reliability_df.copy()\n",
    "rel['train_line'] = rel['train_line'].apply(lambda x: x[0:len(x) - 2] if x.startswith(\"Green\") else x)\n",
    "rel = rel.merge(ridership_df, on=[\"train_line\",\"date\"])\n",
    "\n",
    "mbta_rating = satisfaction_df[satisfaction_df[\"question_description\"] == \"How would you rate the mbta overall?\"]\n",
    "mbta_rating = mbta_rating[[\"date\",\"satisfaction_score\"]]\n",
    "rel = rel.merge(mbta_rating, on=\"date\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
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       "      <td>4.280517e+06</td>\n",
       "      <td>203834.144485</td>\n",
       "      <td>4.0937</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Green</td>\n",
       "      <td>2019-03-01</td>\n",
       "      <td>0.733476</td>\n",
       "      <td>3.301698e+06</td>\n",
       "      <td>157223.716195</td>\n",
       "      <td>4.0937</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Green</td>\n",
       "      <td>2019-03-01</td>\n",
       "      <td>0.718310</td>\n",
       "      <td>3.301698e+06</td>\n",
       "      <td>157223.716195</td>\n",
       "      <td>4.0937</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Green</td>\n",
       "      <td>2019-03-01</td>\n",
       "      <td>0.785826</td>\n",
       "      <td>3.301698e+06</td>\n",
       "      <td>157223.716195</td>\n",
       "      <td>4.0937</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Green</td>\n",
       "      <td>2019-03-01</td>\n",
       "      <td>0.799810</td>\n",
       "      <td>3.301698e+06</td>\n",
       "      <td>157223.716195</td>\n",
       "      <td>4.0937</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Green</td>\n",
       "      <td>2019-03-01</td>\n",
       "      <td>0.708062</td>\n",
       "      <td>3.301698e+06</td>\n",
       "      <td>157223.716195</td>\n",
       "      <td>4.0937</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Green</td>\n",
       "      <td>2019-03-01</td>\n",
       "      <td>0.725036</td>\n",
       "      <td>3.301698e+06</td>\n",
       "      <td>157223.716195</td>\n",
       "      <td>4.0937</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Green</td>\n",
       "      <td>2019-03-01</td>\n",
       "      <td>0.767389</td>\n",
       "      <td>3.301698e+06</td>\n",
       "      <td>157223.716195</td>\n",
       "      <td>4.0937</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>Green</td>\n",
       "      <td>2019-03-01</td>\n",
       "      <td>0.770657</td>\n",
       "      <td>3.301698e+06</td>\n",
       "      <td>157223.716195</td>\n",
       "      <td>4.0937</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>Blue</td>\n",
       "      <td>2019-03-01</td>\n",
       "      <td>0.932093</td>\n",
       "      <td>1.470129e+06</td>\n",
       "      <td>70006.140312</td>\n",
       "      <td>4.0937</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>Blue</td>\n",
       "      <td>2019-03-01</td>\n",
       "      <td>0.953829</td>\n",
       "      <td>1.470129e+06</td>\n",
       "      <td>70006.140312</td>\n",
       "      <td>4.0937</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>Red</td>\n",
       "      <td>2019-02-01</td>\n",
       "      <td>0.908032</td>\n",
       "      <td>4.695638e+06</td>\n",
       "      <td>247138.860912</td>\n",
       "      <td>4.0248</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>Red</td>\n",
       "      <td>2019-02-01</td>\n",
       "      <td>0.838604</td>\n",
       "      <td>4.695638e+06</td>\n",
       "      <td>247138.860912</td>\n",
       "      <td>4.0248</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>864</th>\n",
       "      <td>Blue</td>\n",
       "      <td>2019-04-01</td>\n",
       "      <td>0.940900</td>\n",
       "      <td>1.637315e+06</td>\n",
       "      <td>74423.407240</td>\n",
       "      <td>4.2227</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>865</th>\n",
       "      <td>Blue</td>\n",
       "      <td>2019-04-01</td>\n",
       "      <td>0.933231</td>\n",
       "      <td>1.637315e+06</td>\n",
       "      <td>74423.407240</td>\n",
       "      <td>4.2227</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>866</th>\n",
       "      <td>Red</td>\n",
       "      <td>2018-01-01</td>\n",
       "      <td>0.881557</td>\n",
       "      <td>4.696090e+06</td>\n",
       "      <td>223623.313287</td>\n",
       "      <td>3.8013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>867</th>\n",
       "      <td>Red</td>\n",
       "      <td>2018-01-01</td>\n",
       "      <td>0.881557</td>\n",
       "      <td>4.696090e+06</td>\n",
       "      <td>223623.313287</td>\n",
       "      <td>3.8013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>868</th>\n",
       "      <td>Red</td>\n",
       "      <td>2018-01-01</td>\n",
       "      <td>0.904095</td>\n",
       "      <td>4.696090e+06</td>\n",
       "      <td>223623.313287</td>\n",
       "      <td>3.8013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>869</th>\n",
       "      <td>Red</td>\n",
       "      <td>2018-01-01</td>\n",
       "      <td>0.904095</td>\n",
       "      <td>4.696090e+06</td>\n",
       "      <td>223623.313287</td>\n",
       "      <td>3.8013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>870</th>\n",
       "      <td>Orange</td>\n",
       "      <td>2018-01-01</td>\n",
       "      <td>0.873900</td>\n",
       "      <td>3.987313e+06</td>\n",
       "      <td>189872.067981</td>\n",
       "      <td>3.8013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>871</th>\n",
       "      <td>Orange</td>\n",
       "      <td>2018-01-01</td>\n",
       "      <td>0.873900</td>\n",
       "      <td>3.987313e+06</td>\n",
       "      <td>189872.067981</td>\n",
       "      <td>3.8013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>872</th>\n",
       "      <td>Orange</td>\n",
       "      <td>2018-01-01</td>\n",
       "      <td>0.903820</td>\n",
       "      <td>3.987313e+06</td>\n",
       "      <td>189872.067981</td>\n",
       "      <td>3.8013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>873</th>\n",
       "      <td>Orange</td>\n",
       "      <td>2018-01-01</td>\n",
       "      <td>0.903820</td>\n",
       "      <td>3.987313e+06</td>\n",
       "      <td>189872.067981</td>\n",
       "      <td>3.8013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>874</th>\n",
       "      <td>Green</td>\n",
       "      <td>2018-01-01</td>\n",
       "      <td>0.759914</td>\n",
       "      <td>3.141136e+06</td>\n",
       "      <td>149577.901777</td>\n",
       "      <td>3.8013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>875</th>\n",
       "      <td>Green</td>\n",
       "      <td>2018-01-01</td>\n",
       "      <td>0.759914</td>\n",
       "      <td>3.141136e+06</td>\n",
       "      <td>149577.901777</td>\n",
       "      <td>3.8013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>876</th>\n",
       "      <td>Green</td>\n",
       "      <td>2018-01-01</td>\n",
       "      <td>0.666411</td>\n",
       "      <td>3.141136e+06</td>\n",
       "      <td>149577.901777</td>\n",
       "      <td>3.8013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>877</th>\n",
       "      <td>Green</td>\n",
       "      <td>2018-01-01</td>\n",
       "      <td>0.666411</td>\n",
       "      <td>3.141136e+06</td>\n",
       "      <td>149577.901777</td>\n",
       "      <td>3.8013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>878</th>\n",
       "      <td>Green</td>\n",
       "      <td>2018-01-01</td>\n",
       "      <td>0.761372</td>\n",
       "      <td>3.141136e+06</td>\n",
       "      <td>149577.901777</td>\n",
       "      <td>3.8013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>879</th>\n",
       "      <td>Green</td>\n",
       "      <td>2018-01-01</td>\n",
       "      <td>0.761372</td>\n",
       "      <td>3.141136e+06</td>\n",
       "      <td>149577.901777</td>\n",
       "      <td>3.8013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>880</th>\n",
       "      <td>Green</td>\n",
       "      <td>2018-01-01</td>\n",
       "      <td>0.784902</td>\n",
       "      <td>3.141136e+06</td>\n",
       "      <td>149577.901777</td>\n",
       "      <td>3.8013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>881</th>\n",
       "      <td>Green</td>\n",
       "      <td>2018-01-01</td>\n",
       "      <td>0.784902</td>\n",
       "      <td>3.141136e+06</td>\n",
       "      <td>149577.901777</td>\n",
       "      <td>3.8013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>882</th>\n",
       "      <td>Green</td>\n",
       "      <td>2018-01-01</td>\n",
       "      <td>0.771874</td>\n",
       "      <td>3.141136e+06</td>\n",
       "      <td>149577.901777</td>\n",
       "      <td>3.8013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>883</th>\n",
       "      <td>Green</td>\n",
       "      <td>2018-01-01</td>\n",
       "      <td>0.771874</td>\n",
       "      <td>3.141136e+06</td>\n",
       "      <td>149577.901777</td>\n",
       "      <td>3.8013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>884</th>\n",
       "      <td>Green</td>\n",
       "      <td>2018-01-01</td>\n",
       "      <td>0.830150</td>\n",
       "      <td>3.141136e+06</td>\n",
       "      <td>149577.901777</td>\n",
       "      <td>3.8013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>885</th>\n",
       "      <td>Green</td>\n",
       "      <td>2018-01-01</td>\n",
       "      <td>0.830150</td>\n",
       "      <td>3.141136e+06</td>\n",
       "      <td>149577.901777</td>\n",
       "      <td>3.8013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>886</th>\n",
       "      <td>Green</td>\n",
       "      <td>2018-01-01</td>\n",
       "      <td>0.820140</td>\n",
       "      <td>3.141136e+06</td>\n",
       "      <td>149577.901777</td>\n",
       "      <td>3.8013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>887</th>\n",
       "      <td>Green</td>\n",
       "      <td>2018-01-01</td>\n",
       "      <td>0.820140</td>\n",
       "      <td>3.141136e+06</td>\n",
       "      <td>149577.901777</td>\n",
       "      <td>3.8013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>888</th>\n",
       "      <td>Green</td>\n",
       "      <td>2018-01-01</td>\n",
       "      <td>0.821250</td>\n",
       "      <td>3.141136e+06</td>\n",
       "      <td>149577.901777</td>\n",
       "      <td>3.8013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>889</th>\n",
       "      <td>Green</td>\n",
       "      <td>2018-01-01</td>\n",
       "      <td>0.821250</td>\n",
       "      <td>3.141136e+06</td>\n",
       "      <td>149577.901777</td>\n",
       "      <td>3.8013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>890</th>\n",
       "      <td>Blue</td>\n",
       "      <td>2018-01-01</td>\n",
       "      <td>0.886446</td>\n",
       "      <td>1.323204e+06</td>\n",
       "      <td>63009.713046</td>\n",
       "      <td>3.8013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>891</th>\n",
       "      <td>Blue</td>\n",
       "      <td>2018-01-01</td>\n",
       "      <td>0.886446</td>\n",
       "      <td>1.323204e+06</td>\n",
       "      <td>63009.713046</td>\n",
       "      <td>3.8013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>892</th>\n",
       "      <td>Blue</td>\n",
       "      <td>2018-01-01</td>\n",
       "      <td>0.858096</td>\n",
       "      <td>1.323204e+06</td>\n",
       "      <td>63009.713046</td>\n",
       "      <td>3.8013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>893</th>\n",
       "      <td>Blue</td>\n",
       "      <td>2018-01-01</td>\n",
       "      <td>0.858096</td>\n",
       "      <td>1.323204e+06</td>\n",
       "      <td>63009.713046</td>\n",
       "      <td>3.8013</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>894 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    train_line        date  pct_reliability     ridership  avg_ridership  \\\n",
       "0          Red  2017-10-01         0.886728  5.898601e+06  268118.214193   \n",
       "1          Red  2017-10-01         0.886728  5.898601e+06  268118.214193   \n",
       "2       Orange  2017-10-01         0.929751  4.872726e+06  221487.546203   \n",
       "3       Orange  2017-10-01         0.929751  4.872726e+06  221487.546203   \n",
       "4        Green  2017-10-01         0.738828  3.976259e+06  180739.060190   \n",
       "5        Green  2017-10-01         0.738828  3.976259e+06  180739.060190   \n",
       "6        Green  2017-10-01         0.763832  3.976259e+06  180739.060190   \n",
       "7        Green  2017-10-01         0.763832  3.976259e+06  180739.060190   \n",
       "8        Green  2017-10-01         0.808401  3.976259e+06  180739.060190   \n",
       "9        Green  2017-10-01         0.808401  3.976259e+06  180739.060190   \n",
       "10       Green  2017-10-01         0.833661  3.976259e+06  180739.060190   \n",
       "11       Green  2017-10-01         0.833661  3.976259e+06  180739.060190   \n",
       "12        Blue  2017-10-01         0.943202  1.686439e+06   76656.317300   \n",
       "13        Blue  2017-10-01         0.943202  1.686439e+06   76656.317300   \n",
       "14         Red  2019-03-01         0.927714  5.149664e+06  245222.111793   \n",
       "15         Red  2019-03-01         0.900835  5.149664e+06  245222.111793   \n",
       "16      Orange  2019-03-01         0.887380  4.280517e+06  203834.144485   \n",
       "17      Orange  2019-03-01         0.883176  4.280517e+06  203834.144485   \n",
       "18       Green  2019-03-01         0.733476  3.301698e+06  157223.716195   \n",
       "19       Green  2019-03-01         0.718310  3.301698e+06  157223.716195   \n",
       "20       Green  2019-03-01         0.785826  3.301698e+06  157223.716195   \n",
       "21       Green  2019-03-01         0.799810  3.301698e+06  157223.716195   \n",
       "22       Green  2019-03-01         0.708062  3.301698e+06  157223.716195   \n",
       "23       Green  2019-03-01         0.725036  3.301698e+06  157223.716195   \n",
       "24       Green  2019-03-01         0.767389  3.301698e+06  157223.716195   \n",
       "25       Green  2019-03-01         0.770657  3.301698e+06  157223.716195   \n",
       "26        Blue  2019-03-01         0.932093  1.470129e+06   70006.140312   \n",
       "27        Blue  2019-03-01         0.953829  1.470129e+06   70006.140312   \n",
       "28         Red  2019-02-01         0.908032  4.695638e+06  247138.860912   \n",
       "29         Red  2019-02-01         0.838604  4.695638e+06  247138.860912   \n",
       "..         ...         ...              ...           ...            ...   \n",
       "864       Blue  2019-04-01         0.940900  1.637315e+06   74423.407240   \n",
       "865       Blue  2019-04-01         0.933231  1.637315e+06   74423.407240   \n",
       "866        Red  2018-01-01         0.881557  4.696090e+06  223623.313287   \n",
       "867        Red  2018-01-01         0.881557  4.696090e+06  223623.313287   \n",
       "868        Red  2018-01-01         0.904095  4.696090e+06  223623.313287   \n",
       "869        Red  2018-01-01         0.904095  4.696090e+06  223623.313287   \n",
       "870     Orange  2018-01-01         0.873900  3.987313e+06  189872.067981   \n",
       "871     Orange  2018-01-01         0.873900  3.987313e+06  189872.067981   \n",
       "872     Orange  2018-01-01         0.903820  3.987313e+06  189872.067981   \n",
       "873     Orange  2018-01-01         0.903820  3.987313e+06  189872.067981   \n",
       "874      Green  2018-01-01         0.759914  3.141136e+06  149577.901777   \n",
       "875      Green  2018-01-01         0.759914  3.141136e+06  149577.901777   \n",
       "876      Green  2018-01-01         0.666411  3.141136e+06  149577.901777   \n",
       "877      Green  2018-01-01         0.666411  3.141136e+06  149577.901777   \n",
       "878      Green  2018-01-01         0.761372  3.141136e+06  149577.901777   \n",
       "879      Green  2018-01-01         0.761372  3.141136e+06  149577.901777   \n",
       "880      Green  2018-01-01         0.784902  3.141136e+06  149577.901777   \n",
       "881      Green  2018-01-01         0.784902  3.141136e+06  149577.901777   \n",
       "882      Green  2018-01-01         0.771874  3.141136e+06  149577.901777   \n",
       "883      Green  2018-01-01         0.771874  3.141136e+06  149577.901777   \n",
       "884      Green  2018-01-01         0.830150  3.141136e+06  149577.901777   \n",
       "885      Green  2018-01-01         0.830150  3.141136e+06  149577.901777   \n",
       "886      Green  2018-01-01         0.820140  3.141136e+06  149577.901777   \n",
       "887      Green  2018-01-01         0.820140  3.141136e+06  149577.901777   \n",
       "888      Green  2018-01-01         0.821250  3.141136e+06  149577.901777   \n",
       "889      Green  2018-01-01         0.821250  3.141136e+06  149577.901777   \n",
       "890       Blue  2018-01-01         0.886446  1.323204e+06   63009.713046   \n",
       "891       Blue  2018-01-01         0.886446  1.323204e+06   63009.713046   \n",
       "892       Blue  2018-01-01         0.858096  1.323204e+06   63009.713046   \n",
       "893       Blue  2018-01-01         0.858096  1.323204e+06   63009.713046   \n",
       "\n",
       "     satisfaction_score  \n",
       "0                4.1243  \n",
       "1                4.1243  \n",
       "2                4.1243  \n",
       "3                4.1243  \n",
       "4                4.1243  \n",
       "5                4.1243  \n",
       "6                4.1243  \n",
       "7                4.1243  \n",
       "8                4.1243  \n",
       "9                4.1243  \n",
       "10               4.1243  \n",
       "11               4.1243  \n",
       "12               4.1243  \n",
       "13               4.1243  \n",
       "14               4.0937  \n",
       "15               4.0937  \n",
       "16               4.0937  \n",
       "17               4.0937  \n",
       "18               4.0937  \n",
       "19               4.0937  \n",
       "20               4.0937  \n",
       "21               4.0937  \n",
       "22               4.0937  \n",
       "23               4.0937  \n",
       "24               4.0937  \n",
       "25               4.0937  \n",
       "26               4.0937  \n",
       "27               4.0937  \n",
       "28               4.0248  \n",
       "29               4.0248  \n",
       "..                  ...  \n",
       "864              4.2227  \n",
       "865              4.2227  \n",
       "866              3.8013  \n",
       "867              3.8013  \n",
       "868              3.8013  \n",
       "869              3.8013  \n",
       "870              3.8013  \n",
       "871              3.8013  \n",
       "872              3.8013  \n",
       "873              3.8013  \n",
       "874              3.8013  \n",
       "875              3.8013  \n",
       "876              3.8013  \n",
       "877              3.8013  \n",
       "878              3.8013  \n",
       "879              3.8013  \n",
       "880              3.8013  \n",
       "881              3.8013  \n",
       "882              3.8013  \n",
       "883              3.8013  \n",
       "884              3.8013  \n",
       "885              3.8013  \n",
       "886              3.8013  \n",
       "887              3.8013  \n",
       "888              3.8013  \n",
       "889              3.8013  \n",
       "890              3.8013  \n",
       "891              3.8013  \n",
       "892              3.8013  \n",
       "893              3.8013  \n",
       "\n",
       "[894 rows x 6 columns]"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rel"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "      <th>date</th>\n",
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       "      <th>train_line</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2018-03-01</td>\n",
       "      <td>1.439092e+06</td>\n",
       "      <td>65413.259469</td>\n",
       "      <td>Blue Line</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2018-03-01</td>\n",
       "      <td>3.400249e+06</td>\n",
       "      <td>154556.756873</td>\n",
       "      <td>Green Line</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2018-03-01</td>\n",
       "      <td>4.220321e+06</td>\n",
       "      <td>191832.785561</td>\n",
       "      <td>Orange Line</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2018-03-01</td>\n",
       "      <td>5.013455e+06</td>\n",
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       "      <td>Red Line</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2018-02-01</td>\n",
       "      <td>1.321300e+06</td>\n",
       "      <td>69542.097748</td>\n",
       "      <td>Blue Line</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "         date     ridership  avg_ridership   train_line\n",
       "2  2018-03-01  1.439092e+06   65413.259469    Blue Line\n",
       "3  2018-03-01  3.400249e+06  154556.756873   Green Line\n",
       "4  2018-03-01  4.220321e+06  191832.785561  Orange Line\n",
       "5  2018-03-01  5.013455e+06  227884.315413     Red Line\n",
       "8  2018-02-01  1.321300e+06   69542.097748    Blue Line"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ridership_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>survey_date</th>\n",
       "      <th>question_description</th>\n",
       "      <th>satisfaction_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2019-05-01</td>\n",
       "      <td>How would you rate your most recent trip?</td>\n",
       "      <td>4.0920</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2019-05-01</td>\n",
       "      <td>How satisfied are you with the MBTA's communic...</td>\n",
       "      <td>4.0201</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2019-05-01</td>\n",
       "      <td>How would you rate the mbta overall?</td>\n",
       "      <td>4.1225</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2019-05-01</td>\n",
       "      <td>The mbta provides reliable public transportati...</td>\n",
       "      <td>3.7520</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2018-01-01</td>\n",
       "      <td>How would you rate the mbta overall?</td>\n",
       "      <td>3.8013</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  survey_date                               question_description  \\\n",
       "0  2019-05-01          How would you rate your most recent trip?   \n",
       "1  2019-05-01  How satisfied are you with the MBTA's communic...   \n",
       "2  2019-05-01               How would you rate the mbta overall?   \n",
       "3  2019-05-01  The mbta provides reliable public transportati...   \n",
       "4  2018-01-01               How would you rate the mbta overall?   \n",
       "\n",
       "   satisfaction_score  \n",
       "0              4.0920  \n",
       "1              4.0201  \n",
       "2              4.1225  \n",
       "3              3.7520  \n",
       "4              3.8013  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "satisfaction_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>train_line</th>\n",
       "      <th>service_date</th>\n",
       "      <th>pct_reliability</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Red</td>\n",
       "      <td>2017-10-01</td>\n",
       "      <td>0.886728</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Orange</td>\n",
       "      <td>2017-10-01</td>\n",
       "      <td>0.929751</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Green-B</td>\n",
       "      <td>2017-10-01</td>\n",
       "      <td>0.738828</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Green-C</td>\n",
       "      <td>2017-10-01</td>\n",
       "      <td>0.763832</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Green-D</td>\n",
       "      <td>2017-10-01</td>\n",
       "      <td>0.808401</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  train_line service_date  pct_reliability\n",
       "0        Red   2017-10-01         0.886728\n",
       "1     Orange   2017-10-01         0.929751\n",
       "2    Green-B   2017-10-01         0.738828\n",
       "3    Green-C   2017-10-01         0.763832\n",
       "4    Green-D   2017-10-01         0.808401"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reliability_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.2. Data Exploration (Lei)\n",
    "* Generate appropriate data visualizations for your key variables identified in the previous section\n",
    "* You should have at least three visualizations (and at least two different visualization types)\n",
    "* For each visualization provide an explanation regarding the variables involved and an interpretation of the graph.\n",
    "* If you are using Plotly, insert your visualizations as images as well (upload the graph images to an online source, e.g. github, and link those in Jupyter Notebook)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "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": [
    "df_rel = reliability_df.merge(ridership_df,right_on=\"date\",left_on=\"service_date\")\n",
    "\n",
    "df_rel.head()\n",
    "ax = sns.scatterplot(x=\"pct_reliability\", y=\"ridership\",\n",
    "                 hue=\"train_line_x\", data=df_rel)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### This is visualizing the first hypothesis: Long arrival delays negatively affect customer satisfaction. \n",
    "Here, long arrival delays are represented with wait time at stop which has a scale from 0 (don't need to wait at all) to 5 (long delays). The reliability of the all lines is simplified as \"Ave_Month_RE_overall\" which is the average of all lines' reliability in a month.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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>service_date</th>\n",
       "      <th>Ave_Month_RE_overall</th>\n",
       "      <th>How would you rate the mbta overall?</th>\n",
       "      <th>Wait time at stop</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>2016-02</td>\n",
       "      <td>0.781771</td>\n",
       "      <td>4.1678</td>\n",
       "      <td>3.7348</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>2016-03</td>\n",
       "      <td>0.763396</td>\n",
       "      <td>4.1228</td>\n",
       "      <td>3.3900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>2016-04</td>\n",
       "      <td>0.806854</td>\n",
       "      <td>4.4165</td>\n",
       "      <td>3.6777</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>2016-05</td>\n",
       "      <td>0.812263</td>\n",
       "      <td>4.3533</td>\n",
       "      <td>3.5577</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>2016-06</td>\n",
       "      <td>0.809461</td>\n",
       "      <td>4.1126</td>\n",
       "      <td>3.4028</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  service_date  Ave_Month_RE_overall  How would you rate the mbta overall?  \\\n",
       "0      2016-02              0.781771                                4.1678   \n",
       "1      2016-03              0.763396                                4.1228   \n",
       "2      2016-04              0.806854                                4.4165   \n",
       "3      2016-05              0.812263                                4.3533   \n",
       "4      2016-06              0.809461                                4.1126   \n",
       "\n",
       "   Wait time at stop  \n",
       "0             3.7348  \n",
       "1             3.3900  \n",
       "2             3.6777  \n",
       "3             3.5577  \n",
       "4             3.4028  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_1st = pd.read_csv(\"resources/reliability_satisfaction_combine.csv\")\n",
    "df_1st.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xa22fb1590>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "\n",
    "# create and display the scatter plot\n",
    "graph1 = sns.scatterplot(x=\"Ave_Month_RE_overall\", y=\"How would you rate the mbta overall?\", data = df_1st)\n",
    "\n",
    "#specify the title\n",
    "title = \"The relationship between Reliability and Overall rating\"\n",
    "\n",
    "#set the title of the plot\n",
    "graph1.set_title(title, size = 16)\n",
    "\n",
    "#add labels to the axes  \n",
    "graph1.set_xlabel(\"Ave_Month_RE_overall\", size = 16)\n",
    "graph1.set_ylabel(\"Overall rating\", size = 16)\n",
    "\n",
    "#add regestion line\n",
    "sns.regplot(x=\"Ave_Month_RE_overall\", y=\"How would you rate the mbta overall?\", data=df_1st)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a248a64d0>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "# create and display the scatter plot\n",
    "graph2 = sns.scatterplot(x=\"Ave_Month_RE_overall\", y=\"Wait time at stop\", data = df_1st)\n",
    "\n",
    "#specify the title\n",
    "title = \"The relationship between Reliability and Wait Time\"\n",
    "\n",
    "#set the title of the plot\n",
    "graph2.set_title(title, size = 16)\n",
    "\n",
    "#add labels to the axes  \n",
    "graph2.set_xlabel(\"Ave_Month_RE_overall\", size = 16)\n",
    "graph2.set_ylabel(\"Wait time at stop\", size = 16)\n",
    "\n",
    "#add regestion line\n",
    "sns.regplot(x=\"Ave_Month_RE_overall\", y=\"Wait time at stop\", data=df_1st)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Ave_Month_RE_overall')"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "# create and display the scatter plot\n",
    "graph3 = sns.scatterplot(x=\"service_date\", y=\"Ave_Month_RE_overall\", \n",
    "                        data = df_1st)\n",
    "\n",
    "#specify the title\n",
    "title = \"The relationship between Reliability and Overall Time\"\n",
    "\n",
    "#set the title of the plot\n",
    "graph3.set_title(title, size = 16)\n",
    "\n",
    "#add labels to the axes  \n",
    "graph3.set_xlabel(\"Time\", size = 16)\n",
    "graph3.set_ylabel(\"Ave_Month_RE_overall\", size = 16)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### This is visualizing the second hypothesis: Congestion negatively affect customer satisfaction."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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>Unnamed: 0</th>\n",
       "      <th>date</th>\n",
       "      <th>ridership</th>\n",
       "      <th>train_line</th>\n",
       "      <th>Vehicle crowdedness</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2016-02-01</td>\n",
       "      <td>1.220639e+06</td>\n",
       "      <td>Blue Line</td>\n",
       "      <td>3.5872</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2016-02-01</td>\n",
       "      <td>3.752289e+06</td>\n",
       "      <td>Green Line</td>\n",
       "      <td>3.5872</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>2016-02-01</td>\n",
       "      <td>4.379649e+06</td>\n",
       "      <td>Orange Line</td>\n",
       "      <td>3.5872</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>2016-02-01</td>\n",
       "      <td>5.132032e+06</td>\n",
       "      <td>Red Line</td>\n",
       "      <td>3.5872</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>2016-03-01</td>\n",
       "      <td>1.510118e+06</td>\n",
       "      <td>Blue Line</td>\n",
       "      <td>3.4033</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0        date     ridership   train_line  Vehicle crowdedness\n",
       "0           1  2016-02-01  1.220639e+06    Blue Line               3.5872\n",
       "1           2  2016-02-01  3.752289e+06   Green Line               3.5872\n",
       "2           3  2016-02-01  4.379649e+06  Orange Line               3.5872\n",
       "3           4  2016-02-01  5.132032e+06     Red Line               3.5872\n",
       "4           5  2016-03-01  1.510118e+06    Blue Line               3.4033"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_2nd = pd.read_csv(\"resources/ridership_congestion_combine.csv\")\n",
    "df_2nd.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'ridership')"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "import seaborn as sns\n",
    "\n",
    "# create and display the line plot\n",
    "graph4 = sns.lineplot(x=\"ridership\", y=\"Vehicle crowdedness\", hue=\"train_line\", data = df_2nd\n",
    "                    , palette = [\"blue\", \"green\", \"orange\", \"red\"])\n",
    "\n",
    "#specify the title\n",
    "title = \"Ridership vs. Congestion\"\n",
    "\n",
    "#set the title of the plot\n",
    "graph4.set_title(title, size = 16)\n",
    "\n",
    "#add labels to the axes  \n",
    "graph4.set_xlabel(\"ridership\", size = 10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.3. Model Construction (Dan)\n",
    "* If you proposed hypotheses, conduct your hypothesis tests\n",
    "* For your machine learning question(s), split data into training, validation, and testing sets (or use cross-validation)\n",
    "* Apply machine learning algorithms (apply at least three algorithms)\n",
    "* Train your algorithms"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [],
   "source": [
    "# df is the dataframe and target_column is the name of the column being targeted\n",
    "def features_and_target(df, target_column):\n",
    "    features = df.drop(target_column, axis = 1)\n",
    "    target = df[target_column]\n",
    "    return features, target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [],
   "source": [
    "features, target = features_and_target(rel, \"satisfaction_score\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Using OneHotEncoder so that we can use our knowledge of which line it is on as a feature\n",
    "# Before it was categorical data which can't be used for regression techniques.\n",
    "\n",
    "from sklearn.preprocessing import OneHotEncoder\n",
    "\n",
    "encoder = OneHotEncoder(sparse = False)\n",
    "\n",
    "encoded_df = encoder.fit_transform(features[['train_line']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['x0_Blue', 'x0_Green', 'x0_Orange', 'x0_Red'], dtype=object)"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "encoder.get_feature_names()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Adding encoder data to the final features dataframe.\n",
    "\n",
    "features_df = pd.DataFrame(encoded_df, columns = encoder.get_feature_names())\n",
    "features_final = pd.concat([features_df, features], ignore_index=True, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Adding column names to the dataframe.\n",
    "\n",
    "features_final\n",
    "features_final.columns = ['Blue', 'Green', 'Orange', 'Red', 'Color', 'Date', 'pct_reliability', 'ridership', 'avg_ridership']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
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       "  Blue Green Orange  Red   Color        Date pct_reliability     ridership  \\\n",
       "0  0.0   0.0    0.0  1.0     Red  2017-10-01        0.886728  5.898601e+06   \n",
       "1  0.0   0.0    0.0  1.0     Red  2017-10-01        0.886728  5.898601e+06   \n",
       "2  0.0   0.0    1.0  0.0  Orange  2017-10-01        0.929751  4.872726e+06   \n",
       "3  0.0   0.0    1.0  0.0  Orange  2017-10-01        0.929751  4.872726e+06   \n",
       "4  0.0   1.0    0.0  0.0   Green  2017-10-01        0.738828  3.976259e+06   \n",
       "\n",
       "   avg_ridership  \n",
       "0  268118.214193  \n",
       "1  268118.214193  \n",
       "2  221487.546203  \n",
       "3  221487.546203  \n",
       "4  180739.060190  "
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "features_final.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [],
   "source": [
    "# get rid of extra columns, Color we used the encoder to show the data, and the date format is not needed.\n",
    "features_final = features_final[['Blue', 'Green', 'Orange', 'Red', 'pct_reliability', 'avg_ridership']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>Red</th>\n",
       "      <th>pct_reliability</th>\n",
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       "  Blue Green Orange  Red pct_reliability  avg_ridership\n",
       "0  0.0   0.0    0.0  1.0        0.886728  268118.214193\n",
       "1  0.0   0.0    0.0  1.0        0.886728  268118.214193\n",
       "2  0.0   0.0    1.0  0.0        0.929751  221487.546203\n",
       "3  0.0   0.0    1.0  0.0        0.929751  221487.546203\n",
       "4  0.0   1.0    0.0  0.0        0.738828  180739.060190"
      ]
     },
     "execution_count": 146,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "features_final.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [],
   "source": [
    "# splitting the data set.\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "def split_the_dataset():\n",
    "    return train_test_split(features_final, target, random_state=3000)\n",
    "\n",
    "X_train, X_test, y_train, y_test = split_the_dataset()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Dictionary of the models we wish to use.\n",
    "\n",
    "from sklearn.linear_model import LinearRegression, Lasso, Ridge \n",
    "from sklearn.neighbors import KNeighborsRegressor\n",
    "from sklearn.svm import SVR\n",
    "\n",
    "estimators = {\"Linear Regression\" : LinearRegression(),\n",
    "             \"Ridge\" : Ridge(),\n",
    "              \"Lasso\" : Lasso(),\n",
    "             \"k-Nearest Neighbor\": KNeighborsRegressor(),\n",
    "             \"Support Vector Machine\" : SVR(gamma=\"scale\")}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Training and testing all the models.\n",
    "\n",
    "from sklearn.metrics import r2_score\n",
    "\n",
    "def regressors_percentage_split():\n",
    "    for estimator_name, estimator_object in estimators.items(): \n",
    "        # Fit the dataset\n",
    "        model = estimator_object.fit(X=X_train, y=y_train)\n",
    "        # Print the testing results\n",
    "        print(f\"{estimator_name}:\")\n",
    "        print(\"\\tR-squared value for training set: \", r2_score(y_train, model.predict(X_train)))\n",
    "        print(\"\\tR-squared value for testing set: \", r2_score(y_test, model.predict(X_test)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Linear Regression:\n",
      "\tR-squared value for training set:  0.08919366817432117\n",
      "\tR-squared value for testing set:  0.03367130715087108\n",
      "Ridge:\n",
      "\tR-squared value for training set:  0.08624659273494995\n",
      "\tR-squared value for testing set:  0.04313196429509292\n",
      "Lasso:\n",
      "\tR-squared value for training set:  0.006530199232265832\n",
      "\tR-squared value for testing set:  -0.002895676959340454\n",
      "k-Nearest Neighbor:\n",
      "\tR-squared value for training set:  0.8426706899102732\n",
      "\tR-squared value for testing set:  0.7453230371060957\n",
      "Support Vector Machine:\n",
      "\tR-squared value for training set:  0.03333921236154602\n",
      "\tR-squared value for testing set:  0.003942010248047856\n"
     ]
    }
   ],
   "source": [
    "regressors_percentage_split()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.4. Model Evaluation (Dan)\n",
    "* As you can see, the results for all the regression algorithims except for kNN have shown that they are very poor at predicting rider satisfaction. Not only are the R-squared value for the testing sets much lower than 0.50 (as a general rule of thumb), the models can't even predict well on the training set. On the other hand, kNN showed decent results with R-squared of 0.84 and 0.74 for the training and testing set. We decided to use the kNN model to continue our research, further trying to optimize this model. There are no testing accuracy means and standard deviations to show since we are using regression algorithims as opposed to classification. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.5. Model Optimization (Dan)\n",
    "* Tune your models using appropriate hyperparameters\n",
    "* Explain why you are doing this (e.g., to avoid overfitting, etc.)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{(('Green',), True), (('Orange',), False), (('Red',), False), (('pct_reliability',), True), (('Blue',), False), (('avg_ridership',), True)}\n"
     ]
    }
   ],
   "source": [
    "from sklearn.feature_selection import SelectKBest\n",
    "from sklearn.feature_selection import f_regression\n",
    "\n",
    "#split data into training and testing sets\n",
    "X_train, X_test, y_train, y_test = train_test_split(features_final, target, random_state=3000)\n",
    "\n",
    "#define a selection method and specify the score function to be f_regression\n",
    "select = SelectKBest(score_func=f_regression, k = 3)\n",
    "select.fit(X_train, y_train)\n",
    "\n",
    "#transform training and testing sets so only the selected features are retained\n",
    "X_train_selected = select.transform(X_train)\n",
    "X_test_selected = select.transform(X_test)\n",
    "\n",
    "# For us to see which features are selected, those with True beside them are important.\n",
    "print(set(zip(features_final.columns, select.get_support())))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {},
   "outputs": [],
   "source": [
    "param_grid = {\"n_neighbors\": [3, 5, 10], \"metric\": [\"euclidean\", \"manhattan\", \"minkowski\"]}\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.neighbors import KNeighborsRegressor\n",
    "\n",
    "def grid_search_kNN():\n",
    "    \n",
    "    grid_search = GridSearchCV(KNeighborsRegressor(), param_grid, cv=5)\n",
    "    \n",
    "    # training done with only selected features retained\n",
    "    grid_search.fit(X=X_train_selected, y=y_train) \n",
    "    \n",
    "    print(\"Best parameters: \", grid_search.best_params_)\n",
    "    \n",
    "    print(\"Training set score with best parameters: \", grid_search.best_score_)\n",
    "\n",
    "    # testing done with only selected features retained\n",
    "    print(\"Test set score with best parameters: \", grid_search.score(X_test_selected, y_test))\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best parameters:  {'metric': 'euclidean', 'n_neighbors': 3}\n",
      "Training set score with best parameters:  0.7455485314082229\n",
      "Test set score with best parameters:  0.8967382992447988\n"
     ]
    }
   ],
   "source": [
    "grid_search_kNN()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Hyperparamter Tuning / Optimization Analysis\n",
    "* Since we decided to go forward with optimizing our kNN model, we used Grid Search to evaluate which parameters would be best for our model. As you can see in the param_grid object, we tested over 3, 5, and 10 n_neighbors and 3 different metrics, euclidean, manhatten, and minkowski. We saw the best results when using 3 neighbors which worried us a little bit because we were hoping to avoid overfitting, because as you decrease the number of neighbors used, the model becomes more complex. Furthermore, on the side, we checked to see if the model would prefer using even less neighbors and it did, with 1 neighbor receiving the best score. Obviously, we omitted it from the tested parameters because we want to avoid an overfitted model. Also, interestingly enough, the model actually got worse over the training set, going from a score of roughly 0.84 to 0.74, but, the test set score increased from roughly 0.74 to 0.89."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id=\"4\"></a>\n",
    "<hr style=\"height:2px; border:none; color:black; background-color:black;\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. DISCUSSION\n",
    "* Provide a summary of the steps you took to analyze your data and test your predictive model\n",
    "* Intepret your findings from 3.4., 3.5, and 3.6\n",
    "    * Which algorithms did you compare?\n",
    "    * Which algorithm(s) revealed best performance?\n",
    "    * Which algorithm(s) should be used for your predictive model?\n",
    "* If you tested hypotheses, interpret the results. What does it mean to have significant/non-significant differences with regards to your data?\n",
    "\n",
    "\n",
    "* End this section with a conclusion paragraph containing some pointers for future work \n",
    "    *(e.g., get more data, perform another analysis, etc.)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id=\"5\"></a>\n",
    "<hr style=\"height:2px; border:none; color:black; background-color:black;\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### CONTRIBUTIONS\n",
    "* Describe each team member's contributions to the report (who did what in each section)\n",
    "* Remember this is a team effort!\n",
    "* Each member of your team will provide peer evaluation of other team members. Your final grade on the project will be based on those peer evaluations. "
   ]
  }
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