solution edits

API/1-API/solution/notebook.ipynb

@@ -38,7 +38,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 77,
+   "execution_count": 53,
    "metadata": {},
    "outputs": [
     {
@@ -76,7 +76,7 @@
       "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>datetime</th>\n      <th>city</th>\n      <th>state</th>\n      <th>country</th>\n      <th>shape</th>\n      <th>duration (seconds)</th>\n      <th>duration (hours/min)</th>\n      <th>comments</th>\n      <th>date posted</th>\n      <th>latitude</th>\n      <th>longitude</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>10/10/1949 20:30</td>\n      <td>san marcos</td>\n      <td>tx</td>\n      <td>us</td>\n      <td>cylinder</td>\n      <td>2700.0</td>\n      <td>45 minutes</td>\n      <td>This event took place in early fall around 194...</td>\n      <td>4/27/2004</td>\n      <td>29.883056</td>\n      <td>-97.941111</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>10/10/1949 21:00</td>\n      <td>lackland afb</td>\n      <td>tx</td>\n      <td>NaN</td>\n      <td>light</td>\n      <td>7200.0</td>\n      <td>1-2 hrs</td>\n      <td>1949 Lackland AFB&amp;#44 TX.  Lights racing acros...</td>\n      <td>12/16/2005</td>\n      <td>29.384210</td>\n      <td>-98.581082</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>10/10/1955 17:00</td>\n      <td>chester (uk/england)</td>\n      <td>NaN</td>\n      <td>gb</td>\n      <td>circle</td>\n      <td>20.0</td>\n      <td>20 seconds</td>\n      <td>Green/Orange circular disc over Chester&amp;#44 En...</td>\n      <td>1/21/2008</td>\n      <td>53.200000</td>\n      <td>-2.916667</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>10/10/1956 21:00</td>\n      <td>edna</td>\n      <td>tx</td>\n      <td>us</td>\n      <td>circle</td>\n      <td>20.0</td>\n      <td>1/2 hour</td>\n      <td>My older brother and twin sister were leaving ...</td>\n      <td>1/17/2004</td>\n      <td>28.978333</td>\n      <td>-96.645833</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>10/10/1960 20:00</td>\n      <td>kaneohe</td>\n      <td>hi</td>\n      <td>us</td>\n      <td>light</td>\n      <td>900.0</td>\n      <td>15 minutes</td>\n      <td>AS a Marine 1st Lt. flying an FJ4B fighter/att...</td>\n      <td>1/22/2004</td>\n      <td>21.418056</td>\n      <td>-157.803611</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
      },
      "metadata": {},
-     "execution_count": 77
+     "execution_count": 53
     }
    ],
    "source": [
@@ -90,7 +90,33 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 78,
+   "execution_count": 54,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "array(['us', nan, 'gb', 'ca', 'au', 'de'], dtype=object)"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 54
+    }
+   ],
+   "source": [
+    "from sklearn.preprocessing import LabelEncoder\n",
+    "\n",
+    "ufos = pd.DataFrame({'Seconds': ufos['duration (seconds)'], 'Country': ufos['country'],'Latitude': ufos['latitude'],'Longitude': ufos['longitude']})\n",
+    "\n",
+    "ufos.Country.unique()\n",
+    "\n",
+    "# 0 au, 1 ca, 2 de, 3 gb, 4 us"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 55,
    "metadata": {},
    "outputs": [
     {
@@ -102,10 +128,6 @@
     }
    ],
    "source": [
-    "from sklearn.preprocessing import LabelEncoder\n",
-    "\n",
-    "ufos = pd.DataFrame({'Seconds': ufos['duration (seconds)'], 'Country': ufos['country'],'Latitude': ufos['latitude'],'Longitude': ufos['longitude']})\n",
-    "\n",
     "ufos.dropna(inplace=True)\n",
     "\n",
     "ufos['Country'] = LabelEncoder().fit_transform(ufos['Country'])\n",
@@ -114,14 +136,12 @@
     "\n",
     "ufos = ufos[(ufos['Seconds'] >= 1) & (ufos['Seconds'] <= 60)]\n",
     "\n",
-    "ufos.info()\n",
-    "\n",
-    "\n"
+    "ufos.info()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 79,
+   "execution_count": 56,
    "metadata": {},
    "outputs": [
     {
@@ -138,7 +158,7 @@
       "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>Seconds</th>\n      <th>Country</th>\n      <th>Latitude</th>\n      <th>Longitude</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>2</th>\n      <td>20.0</td>\n      <td>3</td>\n      <td>53.200000</td>\n      <td>-2.916667</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>20.0</td>\n      <td>4</td>\n      <td>28.978333</td>\n      <td>-96.645833</td>\n    </tr>\n    <tr>\n      <th>14</th>\n      <td>30.0</td>\n      <td>4</td>\n      <td>35.823889</td>\n      <td>-80.253611</td>\n    </tr>\n    <tr>\n      <th>23</th>\n      <td>60.0</td>\n      <td>4</td>\n      <td>45.582778</td>\n      <td>-122.352222</td>\n    </tr>\n    <tr>\n      <th>24</th>\n      <td>3.0</td>\n      <td>3</td>\n      <td>51.783333</td>\n      <td>-0.783333</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
      },
      "metadata": {},
-     "execution_count": 79
+     "execution_count": 56
     }
    ],
    "source": [
@@ -152,12 +172,14 @@
     "\n",
     "new_ufos['Country'] = LabelEncoder().fit_transform(new_ufos['Country'])\n",
     "\n",
+    "\n",
+    "\n",
     "new_ufos.head()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 88,
+   "execution_count": 57,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -169,12 +191,12 @@
     "y = new_ufos['Country']\n",
     "\n",
     "\n",
-    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)"
+    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 89,
+   "execution_count": 58,
    "metadata": {},
    "outputs": [
     {
@@ -214,12 +236,13 @@
     "\n",
     "print(classification_report(y_test, predictions))\n",
     "print('Predicted labels: ', predictions)\n",
-    "print('Accuracy: ', accuracy_score(y_test, predictions))\n"
+    "print('Accuracy: ', accuracy_score(y_test, predictions))\n",
+    "\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 97,
+   "execution_count": 59,
    "metadata": {},
    "outputs": [
     {