diff --git a/Presentation.ipynb b/Presentation.ipynb
new file mode 100644
index 0000000..659284e
--- /dev/null
+++ b/Presentation.ipynb
@@ -0,0 +1,742 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Final Project\n",
+    "\n",
+    "### For our final project we decided that we were going to try and find the best Pittsburgh neighborhood to start a family in. To do this we used three metircs. The first one was Park Acreage, the second one was Police Arrest History and the third one being the walkscore of each neighborhood.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Park Acreage\n",
+    "### We chose to analyze the access to parks in the neighborhoods of Pittsburgh to decide the best neighborhood to start a family. This data is relevant because having ample access to parks is essential for raising a child \n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x7faa0d4bb0d0>"
+      ]
+     },
+     "execution_count": 1,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "parks = pd.read_csv(\"parks.csv\")\n",
+    "parks.groupby('sector')['acreage'].sum().plot.bar()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "| SECTOR NUMBER | NEIGHBORHOOD       |\n",
+    "|---------------|---------------------|\n",
+    "| 1             | Troy Hill           |\n",
+    "| 2             | Perry North         |\n",
+    "| 3             | North Shore         |\n",
+    "| 4             | Ingram / Crafton    |\n",
+    "| 5             | Dormont             |\n",
+    "| 6             | Mount Washington    |\n",
+    "| 7             | Mount Oliver / Saint Clair      |\n",
+    "| 8             | Mount Oliver / Saint Clair         |\n",
+    "| 9             | Greenfield          |\n",
+    "| 10            | Squirrel Hill       |\n",
+    "| 11            | Homewood            |\n",
+    "| 12            | Highland Park       |\n",
+    "| 13            | Polish Hill         |\n",
+    "| 14            | Oakland             |\n",
+    "| 15            | Hill District       |\n",
+    "| 16            | Downtown Pittsburgh |"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### In this bar graph you can see that Mount Oliver / Saint Clair is the second best in regards to access to parks. This shows that there is ample park space for you to play with your child in Mount Oliver making it a great place to start a family. We deem it one of the best areas in regards to park acreage if not the best becuase the only neighborhood ahead of it is Squirrel Hill which is only so high because it has such a large sector that contains two major parks, Frick Park and Schenely Park."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "![](mountOpark.jpg)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Police Arrest History\n",
+    "### When trying to decide what metric to focus on to find the best neighborhood to start a family in. We decided to use police arrest history based on each area to help us decide which neighborhood would be the safest, as safety is a number one priority when it comes to raising children."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "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>PK</th>\n",
+       "      <th>CCR</th>\n",
+       "      <th>AGE</th>\n",
+       "      <th>GENDER</th>\n",
+       "      <th>RACE</th>\n",
+       "      <th>ARRESTTIME</th>\n",
+       "      <th>ARRESTLOCATION</th>\n",
+       "      <th>OFFENSES</th>\n",
+       "      <th>INCIDENTLOCATION</th>\n",
+       "      <th>INCIDENTNEIGHBORHOOD</th>\n",
+       "      <th>INCIDENTZONE</th>\n",
+       "      <th>INCIDENTTRACT</th>\n",
+       "      <th>COUNCIL_DISTRICT</th>\n",
+       "      <th>PUBLIC_WORKS_DIVISION</th>\n",
+       "      <th>X</th>\n",
+       "      <th>Y</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1975272</td>\n",
+       "      <td>16158872</td>\n",
+       "      <td>42.0</td>\n",
+       "      <td>F</td>\n",
+       "      <td>B</td>\n",
+       "      <td>2016-08-24T12:20:00</td>\n",
+       "      <td>4700 Block Centre AV Pittsburgh, PA 15213</td>\n",
+       "      <td>3929 Retail Theft.</td>\n",
+       "      <td>4700 Block Centre AV Pittsburgh, PA 15213</td>\n",
+       "      <td>Bloomfield</td>\n",
+       "      <td>5</td>\n",
+       "      <td>804.0</td>\n",
+       "      <td>8.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>-79.949277</td>\n",
+       "      <td>40.452551</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1974456</td>\n",
+       "      <td>16144120</td>\n",
+       "      <td>31.0</td>\n",
+       "      <td>M</td>\n",
+       "      <td>W</td>\n",
+       "      <td>2016-08-03T14:55:00</td>\n",
+       "      <td>4200 Block Steubenville PKE Pittsburgh, PA 15205</td>\n",
+       "      <td>13(a)(16) Possession of Controlled Substance</td>\n",
+       "      <td>4200 Block Steubenville PKE Pittsburgh, PA 15205</td>\n",
+       "      <td>Outside City</td>\n",
+       "      <td>OSC</td>\n",
+       "      <td>5599.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>-80.088018</td>\n",
+       "      <td>40.440136</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>1974466</td>\n",
+       "      <td>16144165</td>\n",
+       "      <td>63.0</td>\n",
+       "      <td>F</td>\n",
+       "      <td>B</td>\n",
+       "      <td>2016-08-03T16:45:00</td>\n",
+       "      <td>900 Block Freeport RD Fox Chapel, PA 15238</td>\n",
+       "      <td>3929 Retail Theft.</td>\n",
+       "      <td>900 Block Freeport RD Fox Chapel, PA 15238</td>\n",
+       "      <td>Westwood</td>\n",
+       "      <td>5</td>\n",
+       "      <td>2811.0</td>\n",
+       "      <td>9.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>-79.891803</td>\n",
+       "      <td>40.486625</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>1974550</td>\n",
+       "      <td>16145257</td>\n",
+       "      <td>25.0</td>\n",
+       "      <td>F</td>\n",
+       "      <td>W</td>\n",
+       "      <td>2016-08-05T02:36:00</td>\n",
+       "      <td>Foreland ST &amp; Cedar AV Pittsburgh, PA 15212</td>\n",
+       "      <td>5503 Disorderly Conduct. / 5505 Public Drunken...</td>\n",
+       "      <td>Foreland ST &amp; Cedar AV Pittsburgh, PA 15212</td>\n",
+       "      <td>East Allegheny</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2304.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>-80.001939</td>\n",
+       "      <td>40.454080</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>1974596</td>\n",
+       "      <td>16145962</td>\n",
+       "      <td>25.0</td>\n",
+       "      <td>M</td>\n",
+       "      <td>B</td>\n",
+       "      <td>2016-08-06T02:00:00</td>\n",
+       "      <td>900 Block Woodlow ST Pittsburgh, PA 15205</td>\n",
+       "      <td>2702 Aggravated Assault. / 2705 Recklessy Enda...</td>\n",
+       "      <td>900 Block Woodlow ST Pittsburgh, PA 15205</td>\n",
+       "      <td>Crafton Heights</td>\n",
+       "      <td>5</td>\n",
+       "      <td>2814.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>-80.052204</td>\n",
+       "      <td>40.445900</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "        PK       CCR   AGE GENDER RACE           ARRESTTIME  \\\n",
+       "0  1975272  16158872  42.0      F    B  2016-08-24T12:20:00   \n",
+       "1  1974456  16144120  31.0      M    W  2016-08-03T14:55:00   \n",
+       "2  1974466  16144165  63.0      F    B  2016-08-03T16:45:00   \n",
+       "3  1974550  16145257  25.0      F    W  2016-08-05T02:36:00   \n",
+       "4  1974596  16145962  25.0      M    B  2016-08-06T02:00:00   \n",
+       "\n",
+       "                                     ARRESTLOCATION  \\\n",
+       "0         4700 Block Centre AV Pittsburgh, PA 15213   \n",
+       "1  4200 Block Steubenville PKE Pittsburgh, PA 15205   \n",
+       "2        900 Block Freeport RD Fox Chapel, PA 15238   \n",
+       "3       Foreland ST & Cedar AV Pittsburgh, PA 15212   \n",
+       "4         900 Block Woodlow ST Pittsburgh, PA 15205   \n",
+       "\n",
+       "                                            OFFENSES  \\\n",
+       "0                                 3929 Retail Theft.   \n",
+       "1       13(a)(16) Possession of Controlled Substance   \n",
+       "2                                 3929 Retail Theft.   \n",
+       "3  5503 Disorderly Conduct. / 5505 Public Drunken...   \n",
+       "4  2702 Aggravated Assault. / 2705 Recklessy Enda...   \n",
+       "\n",
+       "                                   INCIDENTLOCATION INCIDENTNEIGHBORHOOD  \\\n",
+       "0         4700 Block Centre AV Pittsburgh, PA 15213           Bloomfield   \n",
+       "1  4200 Block Steubenville PKE Pittsburgh, PA 15205         Outside City   \n",
+       "2        900 Block Freeport RD Fox Chapel, PA 15238             Westwood   \n",
+       "3       Foreland ST & Cedar AV Pittsburgh, PA 15212       East Allegheny   \n",
+       "4         900 Block Woodlow ST Pittsburgh, PA 15205      Crafton Heights   \n",
+       "\n",
+       "  INCIDENTZONE  INCIDENTTRACT  COUNCIL_DISTRICT  PUBLIC_WORKS_DIVISION  \\\n",
+       "0            5          804.0               8.0                    2.0   \n",
+       "1          OSC         5599.0               NaN                    NaN   \n",
+       "2            5         2811.0               9.0                    2.0   \n",
+       "3            1         2304.0               1.0                    1.0   \n",
+       "4            5         2814.0               2.0                    5.0   \n",
+       "\n",
+       "           X          Y  \n",
+       "0 -79.949277  40.452551  \n",
+       "1 -80.088018  40.440136  \n",
+       "2 -79.891803  40.486625  \n",
+       "3 -80.001939  40.454080  \n",
+       "4 -80.052204  40.445900  "
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#First we imported pandas\n",
+    "import pandas as pd\n",
+    "\n",
+    "%matplotlib inline\n",
+    "\n",
+    "safety = pd.read_csv(\"arrests.csv\")\n",
+    "safety.head(5)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n",
+    "### Next I decided to narrow down the data to make it easier to read and to show that the data contains all types of offenses.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "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>INCIDENTNEIGHBORHOOD</th>\n",
+       "      <th>OFFENSES</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Bloomfield</td>\n",
+       "      <td>3929 Retail Theft.</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Outside City</td>\n",
+       "      <td>13(a)(16) Possession of Controlled Substance</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Westwood</td>\n",
+       "      <td>3929 Retail Theft.</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>East Allegheny</td>\n",
+       "      <td>5503 Disorderly Conduct. / 5505 Public Drunken...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Crafton Heights</td>\n",
+       "      <td>2702 Aggravated Assault. / 2705 Recklessy Enda...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>58317</th>\n",
+       "      <td>Carrick</td>\n",
+       "      <td>2709(a)(1) Harassment by Physical Contact, or ...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>58318</th>\n",
+       "      <td>Carrick</td>\n",
+       "      <td>2701(a)(1) Simple Assault - Intent., Know., Re...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>58319</th>\n",
+       "      <td>Carrick</td>\n",
+       "      <td>2709(a)(1) Harassment by Physical Contact, or ...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>58320</th>\n",
+       "      <td>Central Business District</td>\n",
+       "      <td>9015 Failure To Appear/Arrest on Attachment Order</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>58321</th>\n",
+       "      <td>Homewood North</td>\n",
+       "      <td>2701 Simple Assault.</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>58322 rows × 2 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "            INCIDENTNEIGHBORHOOD  \\\n",
+       "0                     Bloomfield   \n",
+       "1                   Outside City   \n",
+       "2                       Westwood   \n",
+       "3                 East Allegheny   \n",
+       "4                Crafton Heights   \n",
+       "...                          ...   \n",
+       "58317                    Carrick   \n",
+       "58318                    Carrick   \n",
+       "58319                    Carrick   \n",
+       "58320  Central Business District   \n",
+       "58321             Homewood North   \n",
+       "\n",
+       "                                                OFFENSES  \n",
+       "0                                     3929 Retail Theft.  \n",
+       "1           13(a)(16) Possession of Controlled Substance  \n",
+       "2                                     3929 Retail Theft.  \n",
+       "3      5503 Disorderly Conduct. / 5505 Public Drunken...  \n",
+       "4      2702 Aggravated Assault. / 2705 Recklessy Enda...  \n",
+       "...                                                  ...  \n",
+       "58317  2709(a)(1) Harassment by Physical Contact, or ...  \n",
+       "58318  2701(a)(1) Simple Assault - Intent., Know., Re...  \n",
+       "58319  2709(a)(1) Harassment by Physical Contact, or ...  \n",
+       "58320  9015 Failure To Appear/Arrest on Attachment Order  \n",
+       "58321                               2701 Simple Assault.  \n",
+       "\n",
+       "[58322 rows x 2 columns]"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#Converted the data into a DataFrame\n",
+    "incident_loc = pd.DataFrame(safety)\n",
+    "#Selected the name of the column I wanted\n",
+    "incident_loc[['INCIDENTNEIGHBORHOOD','OFFENSES']]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Now that we have seen that the data consists of all types of offenses, we created a graph showing arrest history based on each neighborhood in Pittsburgh.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x7f67dd44f310>"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1008x360 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "#Plotting a bar graph showing the number of incidents in each neighborhood\n",
+    "\n",
+    "safety.groupby(\"INCIDENTNEIGHBORHOOD\").size().sort_values().plot.bar(figsize=(14,5))\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### After seeing the bar graph it printed out, our next goal was to narrow down our results even more to get a better idea about which neighborhood would be the safest to start a family in."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "INCIDENTNEIGHBORHOOD\n",
+      "Mt. Oliver Neighborhood     2\n",
+      "Troy Hill-Herrs Island      6\n",
+      "Mt. Oliver Boro            18\n",
+      "Central Northside          23\n",
+      "Ridgemont                  32\n",
+      "Regent Square              32\n",
+      "Swisshelm Park             38\n",
+      "New Homestead              38\n",
+      "Chartiers City             38\n",
+      "St. Clair                  45\n",
+      "dtype: int64\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x7f67de991f70>"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "lowest_incidents = safety.groupby(\"INCIDENTNEIGHBORHOOD\").size().sort_values().head(10)\n",
+    "print(lowest_incidents)\n",
+    "lowest_incidents.plot.bar()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Conclusion\n",
+    "\n",
+    "### In Conclusion, we figured that any one of these 10 neighborhoods seen above would be perfect to start a family in as they all have relatively low arrest numbers compared to the rest of the neighborhoods where the count exceeded 1000."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Walking Score\n",
+    "![](walking_track.jpg)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### We chose to analyze the access to walking space in the neighborhoods of Pittsburgh to decide the best neighborhood to start a family. This data is relevant because having ample safe walking space is essential to raising a child"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Census_Tract    481000\n",
+      "Walk_Score          68\n",
+      "Name: 261, dtype: int64\n"
+     ]
+    },
+    {
+     "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>Census_Tract</th>\n",
+       "      <th>Walk_Score</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>402.000000</td>\n",
+       "      <td>402.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>410564.268657</td>\n",
+       "      <td>42.330846</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>188912.364660</td>\n",
+       "      <td>23.404659</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>10300.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>290125.000000</td>\n",
+       "      <td>25.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>459151.500000</td>\n",
+       "      <td>42.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>506275.000000</td>\n",
+       "      <td>58.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>982200.000000</td>\n",
+       "      <td>100.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "        Census_Tract  Walk_Score\n",
+       "count     402.000000  402.000000\n",
+       "mean   410564.268657   42.330846\n",
+       "std    188912.364660   23.404659\n",
+       "min     10300.000000    0.000000\n",
+       "25%    290125.000000   25.000000\n",
+       "50%    459151.500000   42.000000\n",
+       "75%    506275.000000   58.000000\n",
+       "max    982200.000000  100.000000"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "\n",
+    "walk_scores = pd.read_csv(\"walkscores.csv\")\n",
+    "mount_oliver = walk_scores.iloc[261]\n",
+    "print(mount_oliver)\n",
+    "walk_scores.describe()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Here we have shown that the walking score in Mount Oliver is between the 75th percentile and the max score. This has shown that Mount Oliver is more than acceptable for starting a family in regarding the walking score."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "![](walkingscoremap.jpg)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Mount Oliver might not be the best in terms of walking score, but it is certainly above average. Considering how strong is places in the other metrics the good, but not perfect walking score should not be seen as a deterent to starting a family here."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Conclusion"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Alex: As previously mentioned we found Mount Oliver to be the best neighborhood. This is due to the fact that when looking at the arrest history of that neighboorhood, Mount Oliver had one of the lowest incidents. Then when comparing it to the walking score of the neighborhood, it was found to be higher than the 75th percentile of every other neighborhood. Meaning in terms of these two metrics, Mount Oliver would be a perfect neighborhood to start a family in."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Aidan: When raising a child ample park space is essential for raising a child in a healthy, active way. Mount Oliver isn't the best in terms of acreage, but it is the second best. Therefore, since the other two metrics have ranked so highly, we feel like a family can settle for the 2nd best in order to get such strong stats in other metrics. Overall is Mount Oliver is the best place to start a family."
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}