githubtemp5
2 years ago
parent
f8d7246fb2
commit
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"execution_count": 110,
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"outputs": [
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"data": {
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"text/html": [
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"<div>\n",
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"<style scoped>\n",
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" .dataframe tbody tr th:only-of-type {\n",
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" vertical-align: middle;\n",
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" }\n",
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"\n",
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" .dataframe tbody tr th {\n",
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" vertical-align: top;\n",
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" }\n",
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"\n",
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" .dataframe thead th {\n",
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" text-align: right;\n",
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" }\n",
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"</style>\n",
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"<table border=\"1\" class=\"dataframe\">\n",
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" <thead>\n",
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" <tr style=\"text-align: right;\">\n",
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" <th></th>\n",
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" <th>datetime</th>\n",
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" <th>city</th>\n",
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" <th>state</th>\n",
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" <th>country</th>\n",
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" <th>shape</th>\n",
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" <th>duration (seconds)</th>\n",
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" <th>duration (hours/min)</th>\n",
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" <th>comments</th>\n",
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" <th>date posted</th>\n",
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" <th>latitude</th>\n",
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" <th>longitude</th>\n",
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" </tr>\n",
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" </thead>\n",
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" <tbody>\n",
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" <tr>\n",
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" <th>0</th>\n",
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" <td>10/10/1949 20:30</td>\n",
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" <td>san marcos</td>\n",
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" <td>tx</td>\n",
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" <td>us</td>\n",
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" <td>cylinder</td>\n",
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" <td>2700.0</td>\n",
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" <td>45 minutes</td>\n",
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" <td>This event took place in early fall around 194...</td>\n",
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" <td>4/27/2004</td>\n",
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" <td>29.883056</td>\n",
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" <td>-97.941111</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>1</th>\n",
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" <td>10/10/1949 21:00</td>\n",
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" <td>lackland afb</td>\n",
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" <td>tx</td>\n",
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" <td>NaN</td>\n",
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" <td>light</td>\n",
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" <td>7200.0</td>\n",
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" <td>1-2 hrs</td>\n",
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" <td>1949 Lackland AFB&#44 TX. Lights racing acros...</td>\n",
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" <td>12/16/2005</td>\n",
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" <td>29.384210</td>\n",
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" <td>-98.581082</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>2</th>\n",
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" <td>10/10/1955 17:00</td>\n",
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" <td>chester (uk/england)</td>\n",
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" <td>NaN</td>\n",
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" <td>gb</td>\n",
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" <td>circle</td>\n",
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" <td>20.0</td>\n",
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" <td>20 seconds</td>\n",
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" <td>Green/Orange circular disc over Chester&#44 En...</td>\n",
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" <td>1/21/2008</td>\n",
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" <td>53.200000</td>\n",
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" <td>-2.916667</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>3</th>\n",
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" <td>10/10/1956 21:00</td>\n",
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" <td>edna</td>\n",
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" <td>tx</td>\n",
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" <td>us</td>\n",
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" <td>circle</td>\n",
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" <td>20.0</td>\n",
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" <td>1/2 hour</td>\n",
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" <td>My older brother and twin sister were leaving ...</td>\n",
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" <td>1/17/2004</td>\n",
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" <td>28.978333</td>\n",
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" <td>-96.645833</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>4</th>\n",
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" <td>10/10/1960 20:00</td>\n",
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" <td>kaneohe</td>\n",
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" <td>hi</td>\n",
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" <td>us</td>\n",
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" <td>light</td>\n",
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" <td>900.0</td>\n",
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" <td>15 minutes</td>\n",
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" <td>AS a Marine 1st Lt. flying an FJ4B fighter/att...</td>\n",
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" <td>1/22/2004</td>\n",
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" <td>21.418056</td>\n",
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" <td>-157.803611</td>\n",
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" </tr>\n",
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" </tbody>\n",
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"</table>\n",
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"</div>"
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],
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"text/plain": [
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" datetime city state country shape \\\n",
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"0 10/10/1949 20:30 san marcos tx us cylinder \n",
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"1 10/10/1949 21:00 lackland afb tx NaN light \n",
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"2 10/10/1955 17:00 chester (uk/england) NaN gb circle \n",
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"3 10/10/1956 21:00 edna tx us circle \n",
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"4 10/10/1960 20:00 kaneohe hi us light \n",
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"\n",
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" duration (seconds) duration (hours/min) \\\n",
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"0 2700.0 45 minutes \n",
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"1 7200.0 1-2 hrs \n",
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"2 20.0 20 seconds \n",
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"3 20.0 1/2 hour \n",
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"4 900.0 15 minutes \n",
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"\n",
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" comments date posted latitude \\\n",
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"0 This event took place in early fall around 194... 4/27/2004 29.883056 \n",
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"1 1949 Lackland AFB, TX. Lights racing acros... 12/16/2005 29.384210 \n",
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"2 Green/Orange circular disc over Chester, En... 1/21/2008 53.200000 \n",
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"3 My older brother and twin sister were leaving ... 1/17/2004 28.978333 \n",
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"4 AS a Marine 1st Lt. flying an FJ4B fighter/att... 1/22/2004 21.418056 \n",
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"\n",
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" longitude \n",
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"0 -97.941111 \n",
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"1 -98.581082 \n",
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"2 -2.916667 \n",
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"3 -96.645833 \n",
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"4 -157.803611 "
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]
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},
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"execution_count": 110,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"\n",
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"\n",
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"import pandas as pd\n",
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"import numpy as np\n",
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"\n",
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"ufos =pd.read_csv(\"./data/ufos.csv\")\n",
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"ufos.head()\n",
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"\n",
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"\n",
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"\n",
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"\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 111,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"array(['us', nan, 'gb', 'ca', 'au', 'de'], dtype=object)"
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]
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},
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"execution_count": 111,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"ufos = pd.DataFrame({'Seconds': ufos['duration (seconds)'], 'Country': ufos['country'],'Latitude': ufos['latitude'],'Longitude': ufos['longitude']})\n",
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" \n",
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"ufos.Country.unique()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 112,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"<class 'pandas.core.frame.DataFrame'>\n",
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"Index: 25863 entries, 2 to 80330\n",
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"Data columns (total 4 columns):\n",
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" # Column Non-Null Count Dtype \n",
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"--- ------ -------------- ----- \n",
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" 0 Seconds 25863 non-null float64\n",
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" 1 Country 25863 non-null object \n",
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" 2 Latitude 25863 non-null float64\n",
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" 3 Longitude 25863 non-null float64\n",
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"dtypes: float64(3), object(1)\n",
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"memory usage: 1010.3+ KB\n"
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]
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}
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],
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"source": [
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"ufos.dropna(inplace=True)\n",
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" \n",
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"ufos = ufos[(ufos['Seconds'] >= 1) & (ufos['Seconds'] <= 60)]\n",
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" \n",
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"ufos.info()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 113,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"[0, 1, 2, 3, 4]\n",
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"['au' 'ca' 'de' 'gb' 'us']\n"
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]
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}
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],
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"source": [
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"from sklearn.preprocessing import LabelEncoder\n",
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"labelencoder = LabelEncoder()\n",
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"ufos['Country'] = labelencoder.fit_transform(ufos['Country'])\n",
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" \n",
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"ufos.head()\n",
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"countries = [0,1,2,3,4]\n",
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"print(countries)\n",
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"print(labelencoder.inverse_transform(countries))"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 114,
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"metadata": {},
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"outputs": [],
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"source": [
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"from sklearn.model_selection import train_test_split\n",
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"\n",
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"selected_features = [\"Seconds\", \"Latitude\", \"Longitude\"]\n",
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"\n",
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"X = ufos[selected_features]\n",
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"y= ufos[\"Country\"]\n",
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"\n",
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"XTrain, XTest, yTrain, yTest = train_test_split(X, y, test_size=0.2, random_state=0)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 115,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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" precision recall f1-score support\n",
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"\n",
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" 0 1.00 1.00 1.00 41\n",
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" 1 0.83 0.23 0.36 250\n",
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" 2 1.00 1.00 1.00 8\n",
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" 3 1.00 1.00 1.00 131\n",
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" 4 0.96 1.00 0.98 4743\n",
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"\n",
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" accuracy 0.96 5173\n",
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" macro avg 0.96 0.85 0.87 5173\n",
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"weighted avg 0.96 0.96 0.95 5173\n",
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"\n",
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"Predicted labels: [4 4 4 ... 3 4 4]\n",
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"Accuracy: 0.9605644693601392\n"
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]
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},
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"c:\\Users\\billg\\Desktop\\alvinsstuff\\Learning\\ML-For-Beginners\\.venv\\Lib\\site-packages\\sklearn\\linear_model\\_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
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"STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
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"\n",
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"Increase the number of iterations (max_iter) or scale the data as shown in:\n",
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" https://scikit-learn.org/stable/modules/preprocessing.html\n",
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"Please also refer to the documentation for alternative solver options:\n",
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" https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
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" n_iter_i = _check_optimize_result(\n"
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]
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}
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],
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"source": [
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"from sklearn.metrics import accuracy_score, classification_report\n",
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"from sklearn.linear_model import LogisticRegression\n",
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"\n",
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"model = LogisticRegression()\n",
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"model.fit(XTrain, yTrain)\n",
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"\n",
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"preds = model.predict(XTest)\n",
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"\n",
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"print(classification_report(yTest, preds))\n",
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"print('Predicted labels: ', preds)\n",
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"print('Accuracy: ', accuracy_score(yTest, preds))"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 116,
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"metadata": {},
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"outputs": [],
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"source": [
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"import pickle\n",
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"\n",
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"model_filename = \"ufo-model.pkl\"\n",
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"pickle.dump(model, open(model_filename, \"wb\"))\n",
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"\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 117,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"[1]\n"
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]
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},
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"c:\\Users\\billg\\Desktop\\alvinsstuff\\Learning\\ML-For-Beginners\\.venv\\Lib\\site-packages\\sklearn\\base.py:493: UserWarning: X does not have valid feature names, but LogisticRegression was fitted with feature names\n",
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" warnings.warn(\n"
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]
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}
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],
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"source": [
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"model = pickle.load(open(model_filename, \"rb\"))\n",
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"\n",
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"print(model.predict([[50,44,-12]]))"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": ".venv",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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||||||
|
},
|
||||||
|
"file_extension": ".py",
|
||||||
|
"mimetype": "text/x-python",
|
||||||
|
"name": "python",
|
||||||
|
"nbconvert_exporter": "python",
|
||||||
|
"pygments_lexer": "ipython3",
|
||||||
|
"version": "3.12.5"
|
||||||
|
}
|
||||||
|
},
|
||||||
|
"nbformat": 4,
|
||||||
|
"nbformat_minor": 2
|
||||||
|
}
|
||||||
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|
|||||||
|
import numpy as np
|
||||||
|
from flask import Flask, request, render_template
|
||||||
|
import pickle
|
||||||
|
|
||||||
|
app = Flask(__name__)
|
||||||
|
|
||||||
|
model = pickle.load(open("./ufo-model.pkl", "rb"))
|
||||||
|
|
||||||
|
|
||||||
|
@app.route("/")
|
||||||
|
def home():
|
||||||
|
return render_template("index.html")
|
||||||
|
|
||||||
|
|
||||||
|
@app.route("/predict", methods=["POST"])
|
||||||
|
def predict():
|
||||||
|
|
||||||
|
int_features = [int(x) for x in request.form.values()]
|
||||||
|
final_features = [np.array(int_features)]
|
||||||
|
prediction = model.predict(final_features)
|
||||||
|
|
||||||
|
output = prediction[0]
|
||||||
|
|
||||||
|
countries = ["Australia", "Canada", "Germany", "UK", "US"]
|
||||||
|
|
||||||
|
return render_template(
|
||||||
|
"index.html", prediction_text="Likely country: {}".format(countries[output])
|
||||||
|
)
|
||||||
|
|
||||||
|
|
||||||
|
if __name__ == "__main__":
|
||||||
|
app.run(debug=True)
|
||||||
@ -0,0 +1,4 @@
|
|||||||
|
scikit-learn
|
||||||
|
pandas
|
||||||
|
numpy
|
||||||
|
flask
|
||||||
@ -0,0 +1,29 @@
|
|||||||
|
body {
|
||||||
|
width: 100%;
|
||||||
|
height: 100%;
|
||||||
|
font-family: 'Helvetica';
|
||||||
|
background: black;
|
||||||
|
color: #fff;
|
||||||
|
text-align: center;
|
||||||
|
letter-spacing: 1.4px;
|
||||||
|
font-size: 30px;
|
||||||
|
}
|
||||||
|
|
||||||
|
input {
|
||||||
|
min-width: 150px;
|
||||||
|
}
|
||||||
|
|
||||||
|
.grid {
|
||||||
|
width: 300px;
|
||||||
|
border: 1px solid #2d2d2d;
|
||||||
|
display: grid;
|
||||||
|
justify-content: center;
|
||||||
|
margin: 20px auto;
|
||||||
|
}
|
||||||
|
|
||||||
|
.box {
|
||||||
|
color: #fff;
|
||||||
|
background: #2d2d2d;
|
||||||
|
padding: 12px;
|
||||||
|
display: inline-block;
|
||||||
|
}
|
||||||
@ -0,0 +1,30 @@
|
|||||||
|
<!DOCTYPE html>
|
||||||
|
<html>
|
||||||
|
<head>
|
||||||
|
<meta charset="UTF-8">
|
||||||
|
<title>🛸 UFO Appearance Prediction! 👽</title>
|
||||||
|
<link rel="stylesheet" href="{{ url_for('static', filename='css/styles.css') }}">
|
||||||
|
</head>
|
||||||
|
|
||||||
|
<body>
|
||||||
|
<div class="grid">
|
||||||
|
|
||||||
|
<div class="box">
|
||||||
|
|
||||||
|
<p>According to the number of seconds, latitude and longitude, which country is likely to have reported seeing a UFO?</p>
|
||||||
|
|
||||||
|
<form action="{{ url_for('predict')}}" method="post">
|
||||||
|
<input type="number" name="seconds" placeholder="Seconds" required="required" min="0" max="60" />
|
||||||
|
<input type="text" name="latitude" placeholder="Latitude" required="required" />
|
||||||
|
<input type="text" name="longitude" placeholder="Longitude" required="required" />
|
||||||
|
<button type="submit" class="btn">Predict country where the UFO is seen</button>
|
||||||
|
</form>
|
||||||
|
|
||||||
|
<p>{{ prediction_text }}</p>
|
||||||
|
|
||||||
|
</div>
|
||||||
|
|
||||||
|
</div>
|
||||||
|
|
||||||
|
</body>
|
||||||
|
</html>
|
||||||
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Reference in new issue