adding app files

.gitignore

@@ -0,0 +1 @@
+.DS_Store

app.py

@@ -0,0 +1,70 @@
+import streamlit as st
+import pandas as pd
+import numpy as np
+
+
+st.set_page_config(
+    page_title="Hello",
+    page_icon="👋",
+)
+
+# Create a page header
+st.header("Welcome to my homepage! 👋")
+
+
+# Create three columns 
+col1, col2, col3 = st.columns([1,1,1])
+
+
+# inside of the first column
+with col1:
+
+    # display a picture
+    st.image('images/covid-icon.png')
+
+    # display the link to that page.
+    st.write('<a href="/Interactive_Charts"> Check out my Covid Dashboard</a>', unsafe_allow_html=True)
+    
+    # display another picture
+    st.image('images/friends.jpg')
+
+    # display another link to that page
+    st.write('<a href="https://www.behance.net/datatime">View more pretty data visualizations.</a>', unsafe_allow_html=True)
+
+
+# inside of column 2
+with col2:
+    # display a picture
+    st.image('images/covid-map.png')
+
+    # display a link 
+    st.write('<a href="/Map"> Check out my Interactive Map</a>', unsafe_allow_html=True)    
+    
+
+    # same
+    st.image('images/github.png')
+    # same
+    st.write('<a href="https://github.com/zd123"> View more awesome code on my github.</a>', unsafe_allow_html=True)    
+
+
+
+# inside of column 3
+with col3:
+    # st.write('<div style="background:red">asdf </div>', unsafe_allow_html=True)
+    
+    # display a picture
+    st.image('https://www.rmg.co.uk/sites/default/files/styles/full_width_1440/public/Atlantic%20liner%20%27Titanic%27%20%28Br%2C%201912%29%20sinking%2C%20bow%20first%2C%201912%2C%20with%20eight%20full%20lifeboats%20nearby%20and%20an%20iceberg%20in%20the%20distance_banner.jpg?itok=fQV6kN3z')
+
+    # display a link to that page
+    st.write('<a href="/ML_Model">Interact with my ML algorithm.</a>', unsafe_allow_html=True)    
+    
+    # same
+    st.image('https://i1.sndcdn.com/avatars-000034142709-fv26gu-t500x500.jpg')
+    #same
+    st.markdown('<a href="/Bio">Learn more about me as a human :blush:</a>', unsafe_allow_html=True)
+
+
+
+
+
+

data/ny-vs-sf-houses.csv

@@ -0,0 +1,493 @@
+in_sf,beds,bath,price,year_built,sqft,price_per_sqft,elevation,city
+0,2.0,1.0,999000,1960,1000,999,10,NY
+0,2.0,2.0,2750000,2006,1418,1939,0,NY
+0,2.0,2.0,1350000,1900,2150,628,9,NY
+0,1.0,1.0,629000,1903,500,1258,9,NY
+0,0.0,1.0,439000,1930,500,878,10,NY
+0,0.0,1.0,439000,1930,500,878,10,NY
+0,1.0,1.0,475000,1920,500,950,10,NY
+0,1.0,1.0,975000,1930,900,1083,10,NY
+0,1.0,1.0,975000,1930,900,1083,12,NY
+0,2.0,1.0,1895000,1921,1000,1895,12,NY
+0,3.0,3.0,2095000,1926,2200,952,4,NY
+0,1.0,1.0,999000,1982,784,1274,5,NY
+0,1.0,1.0,999000,1982,784,1274,5,NY
+0,1.0,1.0,1249000,1987,826,1512,3,NY
+0,0.0,1.0,1110000,2008,698,1590,5,NY
+0,2.0,2.0,2059500,2008,1373,1500,5,NY
+0,2.0,2.0,2000000,1928,1200,1667,10,NY
+0,1.0,1.0,715000,1903,557,1284,3,NY
+0,2.0,2.0,2498000,2005,1260,1983,2,NY
+0,2.0,1.5,2650000,1915,2500,1060,5,NY
+0,2.0,2.0,3450000,1900,1850,1865,9,NY
+0,1.0,1.0,3105000,2016,1108,2802,10,NY
+0,4.0,5.0,13750000,2016,3699,3717,10,NY
+0,2.0,2.0,1185000,1900,1000,1185,4,NY
+0,1.0,2.0,1699000,1900,1500,1133,5,NY
+0,1.0,1.0,1195000,1900,1093,1093,6,NY
+0,1.0,1.0,450000,1920,500,900,10,NY
+0,1.0,1.0,1195000,1900,1093,1093,10,NY
+0,2.0,2.0,1185000,1900,1000,1185,10,NY
+0,0.0,1.0,625000,1964,800,781,8,NY
+0,2.0,2.0,3159990,2012,1420,2225,8,NY
+0,2.0,2.0,2200000,1920,2050,1073,10,NY
+0,2.0,2.0,4895000,2016,1520,3220,10,NY
+0,2.0,2.0,6525000,2016,2018,3233,10,NY
+0,2.0,2.0,4895000,2016,1520,3220,11,NY
+0,2.0,2.0,6525000,2016,2018,3233,11,NY
+0,2.0,2.0,1675000,2006,1225,1367,12,NY
+0,2.0,2.0,1675000,2006,1225,1367,12,NY
+0,2.0,1.0,999000,1985,799,1250,5,NY
+0,1.0,1.0,1550000,1926,1000,1550,6,NY
+0,1.0,1.0,1595000,2015,714,2234,7,NY
+0,2.0,2.0,3995000,1906,2400,1665,9,NY
+0,1.0,1.0,1285000,2012,749,1716,10,NY
+0,1.0,1.0,1595000,2015,714,2234,10,NY
+0,2.0,2.0,3995000,1906,2400,1665,10,NY
+0,2.0,2.0,4995000,1900,2024,2468,8,NY
+0,3.0,2.0,3580000,1880,3000,1193,10,NY
+0,3.0,3.0,6350000,2015,2500,2540,3,NY
+0,3.0,3.0,6550000,2015,2500,2620,3,NY
+0,3.0,3.0,5985000,1909,3300,1814,3,NY
+0,3.0,3.0,9900000,2015,2950,3356,4,NY
+0,1.0,1.0,1849000,1920,1400,1321,5,NY
+0,2.0,2.0,3500000,1915,2000,1750,5,NY
+0,2.0,2.0,3500000,1910,1887,1855,5,NY
+0,1.0,1.0,1250000,2007,720,1736,6,NY
+0,2.0,1.0,899000,1990,820,1096,7,NY
+0,1.0,2.0,1950000,1900,1300,1500,7,NY
+0,1.0,1.0,1750000,1963,1000,1750,5,NY
+0,0.0,1.0,775000,2009,546,1419,6,NY
+0,0.0,1.0,390000,1955,550,709,6,NY
+0,0.0,1.0,699999,1988,500,1400,9,NY
+0,1.0,1.0,649000,1965,750,865,10,NY
+0,2.0,2.0,1200000,1964,1200,1000,10,NY
+0,0.0,1.0,319000,1941,500,638,10,NY
+0,0.0,1.0,699999,1988,500,1400,10,NY
+0,0.0,1.0,775000,2009,546,1419,10,NY
+0,1.0,1.0,649000,1965,750,865,10,NY
+0,2.0,2.0,1849000,2009,1135,1629,10,NY
+0,1.0,1.0,615000,1960,750,820,11,NY
+0,1.0,1.0,1590000,2008,785,2025,12,NY
+0,1.0,1.0,1150000,2014,645,1783,18,NY
+0,2.0,2.0,2650000,2014,1240,2137,18,NY
+0,0.0,1.0,469000,1932,500,938,18,NY
+0,0.0,1.0,569000,1962,543,1048,8,NY
+0,1.0,1.0,549000,1924,750,732,10,NY
+0,2.0,2.0,799000,1928,850,940,19,NY
+0,2.0,2.0,879000,1928,800,1099,22,NY
+0,2.0,3.0,2999000,1925,3200,937,10,NY
+0,2.0,3.0,2999000,1925,3200,937,10,NY
+0,2.0,3.0,2999000,1925,3200,937,12,NY
+0,2.0,3.0,2999000,1925,3200,937,12,NY
+0,1.0,1.0,399000,1910,475,840,10,NY
+0,1.0,1.0,1135000,2005,715,1587,10,NY
+0,1.0,1.0,1145000,2005,700,1636,10,NY
+0,1.0,1.0,1760000,1969,950,1853,10,NY
+0,2.0,2.0,1725000,2005,976,1767,10,NY
+0,2.0,2.0,2750000,2007,1384,1987,10,NY
+0,2.0,2.0,2750000,2007,1384,1987,13,NY
+0,1.0,1.0,399000,1910,475,840,14,NY
+0,2.0,2.0,1725000,2005,1144,1508,16,NY
+0,1.0,1.0,869000,1988,670,1297,16,NY
+0,1.0,1.0,1760000,1969,950,1853,17,NY
+0,3.0,3.0,1249000,1962,1500,833,18,NY
+0,3.0,3.0,1500000,1962,1600,938,18,NY
+0,1.0,1.0,1135000,2005,715,1587,19,NY
+0,1.0,1.0,1145000,2005,700,1636,19,NY
+0,2.0,2.0,1725000,2005,976,1767,19,NY
+0,2.0,2.0,3500000,1925,1550,2258,20,NY
+0,0.0,1.0,525000,1940,525,1000,21,NY
+0,2.0,2.0,2025000,1940,1433,1413,21,NY
+0,2.0,2.0,3500000,1986,1463,2392,23,NY
+0,0.0,1.0,925000,1978,585,1581,24,NY
+0,2.0,2.0,1700000,1982,1007,1688,25,NY
+0,2.0,2.0,1700000,1982,1007,1688,25,NY
+0,0.0,1.0,449000,1962,550,816,10,NY
+0,0.0,1.0,299000,1930,400,748,12,NY
+0,1.0,1.0,695000,1961,720,965,16,NY
+0,1.0,1.0,695000,1961,720,965,16,NY
+0,4.0,5.0,17750000,2012,4476,3966,20,NY
+0,5.0,5.0,27500000,1930,7500,3667,21,NY
+0,0.0,1.0,539000,1957,485,1111,10,NY
+0,0.0,1.0,779000,1975,512,1521,10,NY
+0,1.0,1.0,925000,1985,745,1242,10,NY
+0,1.0,1.0,1319000,1958,1000,1319,10,NY
+0,2.0,2.0,4240000,2016,1741,2435,10,NY
+0,2.0,2.0,4285000,2016,1747,2453,10,NY
+0,3.0,3.0,4875000,2016,2017,2417,10,NY
+0,3.0,3.0,14950000,1931,4435,3371,10,NY
+0,3.0,3.0,10225000,2016,3007,3400,10,NY
+0,4.0,4.0,13400000,2016,3331,4023,10,NY
+0,5.0,5.0,19000000,2016,4972,3821,10,NY
+0,0.0,1.0,539000,1957,485,1111,11,NY
+0,1.0,1.0,835000,1963,700,1193,12,NY
+0,10.0,10.0,7995000,1910,6400,1249,12,NY
+0,0.0,1.0,349000,1960,400,873,13,NY
+0,1.0,1.0,588000,1970,600,980,14,NY
+0,2.0,2.0,8690000,2002,2178,3990,15,NY
+0,2.0,2.0,4195000,2016,1750,2397,15,NY
+0,2.0,2.0,4195000,2016,1742,2408,15,NY
+0,2.0,2.0,4240000,2016,1741,2435,15,NY
+0,2.0,2.0,4285000,2016,1747,2453,15,NY
+0,3.0,3.0,4875000,2016,2017,2417,15,NY
+0,3.0,3.0,5750000,2016,2196,2618,15,NY
+0,3.0,3.0,9350000,2016,3054,3062,15,NY
+0,3.0,3.0,10225000,2016,3007,3400,15,NY
+0,4.0,4.0,13150000,2016,3338,3939,15,NY
+0,4.0,4.0,13400000,2016,3331,4023,15,NY
+0,5.0,5.0,19000000,2016,4972,3821,15,NY
+0,0.0,1.0,850000,1924,546,1557,10,NY
+0,7.0,7.0,19500000,1994,4238,4601,10,NY
+0,1.0,1.0,1000000,1912,612,1634,18,NY
+0,0.0,1.0,485000,1902,310,1565,23,NY
+0,3.0,2.0,7350000,1999,2075,3542,23,NY
+0,1.0,1.0,1200000,1969,600,2000,23,NY
+0,2.0,2.0,4250000,2011,1504,2826,23,NY
+0,2.0,2.0,3600000,1960,1340,2687,24,NY
+0,2.0,2.0,3600000,1960,1340,2687,24,NY
+0,1.0,1.0,525000,1924,700,750,24,NY
+0,0.0,1.0,545000,1900,387,1408,10,NY
+0,1.0,1.0,535000,1900,450,1189,10,NY
+0,4.0,4.0,5500000,1923,3200,1719,21,NY
+0,4.0,5.0,12000000,1939,3700,3243,22,NY
+0,1.0,2.0,1850000,2007,839,2205,23,NY
+0,1.0,1.0,535000,1900,450,1189,27,NY
+0,2.0,2.0,1350000,1931,1300,1038,36,NY
+0,1.0,1.0,779000,1902,700,1113,10,NY
+0,2.0,1.0,1475000,1973,971,1519,10,NY
+0,2.0,2.0,1385000,1971,962,1440,10,NY
+0,1.0,1.0,779000,1902,700,1113,13,NY
+0,1.0,1.0,649000,1929,800,811,25,NY
+0,0.0,1.0,725000,1960,600,1208,26,NY
+0,2.0,1.0,925000,1941,790,1171,27,NY
+0,1.0,1.0,965000,1961,787,1226,27,NY
+0,2.0,1.0,1250000,1922,1145,1092,30,NY
+0,1.0,1.0,650000,1907,720,903,32,NY
+0,2.0,1.0,385000,1999,820,470,8,NY
+0,2.0,2.0,1695000,2012,1051,1613,16,NY
+0,1.0,1.0,600000,1899,1060,566,8,NY
+0,2.0,1.0,910000,1899,1060,858,8,NY
+0,8.0,7.0,2300000,1910,4180,550,9,NY
+0,1.0,1.0,600000,1899,1060,566,10,NY
+0,2.0,1.0,910000,1899,1060,858,10,NY
+0,2.0,2.0,1599000,1973,1400,1142,10,NY
+0,2.0,2.0,4625000,1987,1695,2729,10,NY
+0,0.0,1.0,325000,1910,375,867,11,NY
+0,2.0,2.0,1599000,1973,1400,1142,14,NY
+0,2.0,2.0,1965000,2005,1330,1477,16,NY
+0,1.0,1.0,735000,1928,800,919,22,NY
+0,2.0,2.0,4625000,1987,1695,2729,29,NY
+0,1.0,1.0,749000,2011,762,983,10,NY
+0,1.0,1.0,499999,2011,669,747,35,NY
+0,1.0,1.0,749000,2011,762,983,35,NY
+0,2.0,2.0,904000,1920,1503,601,10,NY
+0,2.0,2.0,904000,1920,1503,601,10,NY
+0,2.0,1.0,559900,1925,1200,467,51,NY
+0,2.0,1.0,545000,1939,1049,520,39,NY
+0,2.0,1.0,365000,1925,700,521,73,NY
+0,2.0,1.0,365000,1925,700,521,73,NY
+0,1.0,1.0,935000,1910,1102,848,8,NY
+0,0.0,1.0,820000,2013,533,1538,8,NY
+0,0.0,1.0,835000,2013,501,1667,8,NY
+0,1.0,1.0,935000,1910,1102,848,10,NY
+0,1.0,1.0,1420000,2004,768,1849,10,NY
+0,1.0,1.0,1550000,2004,794,1952,10,NY
+0,2.0,2.0,1635000,2004,957,1708,10,NY
+0,1.0,1.0,1550000,2004,794,1952,11,NY
+0,1.0,1.0,1780000,2007,988,1802,14,NY
+0,2.0,2.0,2800000,2007,1308,2141,14,NY
+0,1.0,1.0,411500,1921,586,702,6,NY
+0,2.0,2.0,2175000,1999,1569,1386,10,NY
+0,2.0,2.0,1995000,1996,1044,1911,10,NY
+0,2.0,2.0,2235000,1999,1548,1444,14,NY
+0,4.0,4.0,8800000,1941,3382,2602,15,NY
+0,2.0,2.0,1850000,1996,1044,1772,17,NY
+0,2.0,2.0,1995000,1996,1044,1911,17,NY
+0,1.0,1.0,1695000,1927,680,2493,17,NY
+0,1.0,1.0,1495000,1962,1125,1329,19,NY
+0,2.0,2.0,2200000,2000,1044,2107,2,NY
+0,0.5,1.0,384900,1962,540,713,10,NY
+0,1.0,1.0,515000,1962,725,710,10,NY
+0,3.0,2.0,1950000,1956,1600,1219,10,NY
+0,0.0,1.0,307000,1910,330,930,15,NY
+0,2.0,2.0,1735000,1980,1585,1095,18,NY
+0,3.0,3.0,1850000,2005,1353,1367,8,NY
+0,3.0,3.0,1850000,2005,1353,1367,10,NY
+0,2.0,2.0,1995000,1986,1406,1419,14,NY
+0,2.0,2.0,2900000,1987,1600,1813,24,NY
+0,2.0,2.0,2499000,2004,1658,1507,24,NY
+0,2.0,2.0,1575000,1930,1324,1190,33,NY
+0,3.0,3.0,1495000,1990,1360,1099,0,NY
+0,1.0,1.0,529000,1986,650,814,0,NY
+0,3.0,3.0,2695000,2006,1991,1354,1,NY
+0,3.0,3.0,2695000,2006,1991,1354,1,NY
+0,4.0,3.0,3895000,2001,2277,1711,0,NY
+1,1.0,1.0,550000,1982,724,760,24,SF
+1,2.0,2.0,849000,1982,1030,824,24,SF
+1,3.0,2.0,1750000,1900,2950,593,26,SF
+1,1.0,1.0,799000,2008,847,943,29,SF
+1,1.0,1.5,899000,1997,1453,619,3,SF
+1,1.0,1.0,598000,2005,534,1120,5,SF
+1,1.0,2.0,1088000,1998,1086,1002,6,SF
+1,1.0,1.0,798000,1926,769,1038,10,SF
+1,1.0,1.0,798000,1926,769,1038,10,SF
+1,1.0,1.0,1495000,1927,2275,657,10,SF
+1,4.0,4.0,4300000,2006,3321,1295,10,SF
+1,1.0,1.0,699000,2008,756,925,12,SF
+1,2.0,2.0,334905,2000,1047,320,12,SF
+1,1.0,1.5,849000,1996,1127,753,13,SF
+1,1.0,1.5,1365000,1996,1607,849,13,SF
+1,1.0,1.0,649000,2011,674,963,14,SF
+1,3.0,3.0,1245000,1907,1503,828,23,SF
+1,1.0,1.0,649000,1983,850,764,163,SF
+1,2.0,2.0,1700000,1987,1250,1360,11,SF
+1,2.0,2.0,1980000,2009,1469,1348,0,SF
+1,2.0,2.0,3600000,2009,1652,2179,0,SF
+1,2.0,3.0,4995000,2009,2230,2240,0,SF
+1,2.0,2.0,1298000,2008,1159,1120,0,SF
+1,2.0,2.0,2149000,2008,1317,1632,0,SF
+1,3.0,2.0,1995000,2006,1362,1465,3,SF
+1,2.0,2.0,1650000,1937,1640,1006,7,SF
+1,1.0,1.0,949000,2010,824,1152,8,SF
+1,1.0,1.0,187518,2000,670,280,12,SF
+1,3.0,2.5,1995000,2008,2354,847,23,SF
+1,3.0,2.5,1995000,2008,2354,847,23,SF
+1,2.0,2.0,2799000,2008,1328,2108,35,SF
+1,2.0,2.5,1050000,2000,1640,640,2,SF
+1,2.0,2.0,895000,2006,1113,804,3,SF
+1,1.0,1.0,998000,2006,872,1144,3,SF
+1,2.0,2.0,1659000,2000,1165,1424,4,SF
+1,1.0,1.0,788000,2004,903,873,4,SF
+1,2.0,2.0,1395000,2001,1334,1046,4,SF
+1,2.0,2.0,1299000,2004,1453,894,5,SF
+1,2.0,2.5,850000,2000,1136,748,5,SF
+1,3.0,2.5,2850000,2013,2075,1373,5,SF
+1,2.0,2.0,950000,2000,1258,755,6,SF
+1,2.0,2.0,1600000,2002,1173,1364,7,SF
+1,3.0,2.0,1275000,2001,1502,849,13,SF
+1,1.0,1.5,775000,2009,835,928,14,SF
+1,3.0,2.0,1399000,1892,1809,773,41,SF
+1,2.0,2.0,879000,1912,950,925,53,SF
+1,1.0,1.0,699000,1907,932,750,59,SF
+1,1.0,1.0,985000,1978,884,1114,11,SF
+1,1.0,1.0,725000,1978,1063,682,60,SF
+1,2.0,2.0,849000,1911,1100,772,66,SF
+1,1.0,1.0,618000,1973,705,877,72,SF
+1,2.0,2.0,5950000,1989,3700,1608,83,SF
+1,4.0,4.0,1800000,1948,2475,727,10,SF
+1,2.0,2.5,2350000,2008,1314,1788,19,SF
+1,1.0,1.0,740200,1920,989,748,41,SF
+1,1.0,1.0,699000,1982,780,896,51,SF
+1,2.0,2.0,958000,2005,915,1047,54,SF
+1,2.0,2.0,1200000,1909,1302,922,56,SF
+1,3.0,3.0,1575000,1993,2233,705,62,SF
+1,2.0,2.0,1199000,1964,1100,1090,68,SF
+1,1.0,1.0,529000,1966,791,669,69,SF
+1,1.0,1.0,699000,1984,880,794,73,SF
+1,3.0,2.0,1295000,1926,1675,773,77,SF
+1,4.0,4.0,1800000,1948,2347,767,87,SF
+1,1.0,1.0,795000,2000,990,803,7,SF
+1,2.0,2.0,779000,2007,808,964,7,SF
+1,1.0,1.5,378551,2000,1000,379,9,SF
+1,4.0,2.3,1398000,1904,2492,561,13,SF
+1,2.0,1.3,1097000,1904,1493,735,14,SF
+1,1.0,1.0,670000,2014,507,1321,20,SF
+1,2.0,2.0,1199000,2014,943,1271,20,SF
+1,2.0,2.0,1095000,2010,1135,965,30,SF
+1,1.0,2.0,795000,2014,616,1291,31,SF
+1,3.0,2.0,979000,1900,1440,680,33,SF
+1,3.0,3.0,1488888,1977,2100,709,38,SF
+1,3.0,2.0,1389000,1908,1546,898,55,SF
+1,3.0,2.0,1389000,1908,1546,898,55,SF
+1,4.0,2.0,1099000,1962,1267,867,69,SF
+1,3.0,3.5,1799000,1900,2449,735,75,SF
+1,3.0,2.0,1250000,1924,1450,862,83,SF
+1,3.0,2.0,998000,1907,1464,682,84,SF
+1,3.0,2.5,1300000,1975,1642,792,4,SF
+1,1.0,1.0,539000,2000,709,760,5,SF
+1,1.0,1.0,735000,1983,779,944,12,SF
+1,3.0,2.0,688000,1942,1441,477,34,SF
+1,2.0,1.0,859000,1890,1100,781,44,SF
+1,3.0,2.0,699000,1964,1250,559,48,SF
+1,2.0,1.0,750000,1924,980,765,48,SF
+1,3.0,2.0,549000,1915,1972,278,51,SF
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+1,4.0,3.0,900000,2003,1870,481,54,SF
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+1,2.0,1.0,599000,1942,707,847,61,SF
+1,3.0,1.0,648800,1940,1325,490,66,SF
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+1,2.0,1.0,798888,1929,1025,779,74,SF
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+1,2.0,1.0,699000,1913,900,777,108,SF
+1,2.0,1.0,699000,1913,900,777,108,SF
+1,2.0,1.0,500000,1958,1166,429,136,SF
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+1,3.0,2.5,879000,1981,2111,416,139,SF
+1,4.0,2.5,699900,1982,1752,399,140,SF
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+1,3.0,2.0,1195000,1907,1396,856,33,SF
+1,3.0,2.0,1195000,1907,1396,856,33,SF
+1,1.0,1.0,697000,1922,694,1004,50,SF
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+1,1.0,1.0,650000,1955,600,1083,74,SF
+1,1.0,1.0,650000,1955,600,1083,74,SF
+1,1.0,1.0,775000,1955,600,1292,74,SF
+1,1.0,1.0,825000,1955,600,1375,74,SF
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+1,2.0,2.0,989000,1984,988,1001,46,SF
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+1,1.0,1.0,725000,1900,811,894,70,SF
+1,1.0,1.0,725000,1900,811,894,70,SF
+1,4.0,4.0,2650000,1900,3816,694,75,SF
+1,2.0,2.0,1680000,1962,1850,908,89,SF
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+1,3.0,2.5,799000,1947,1400,571,35,SF
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+1,2.0,1.0,699000,1944,995,703,41,SF
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+1,3.0,2.0,899000,1948,1665,540,52,SF
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+1,1.0,1.0,599000,1900,624,960,91,SF
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+1,3.0,2.0,995000,2000,1393,714,75,SF
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+1,3.0,3.0,1139000,2008,1532,743,44,SF
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+1,4.0,4.0,1595000,1925,2750,580,81,SF
+1,2.0,1.0,699000,1907,1200,583,14,SF
+1,2.0,1.0,799000,1938,1150,695,36,SF
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+1,2.0,1.0,749000,1936,1450,517,110,SF
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+1,3.0,2.5,929000,2015,1391,668,5,SF
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+1,2.0,2.0,1698000,2008,1620,1048,1,SF
+1,2.0,2.0,1698000,2008,1620,1048,1,SF
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+1,0.0,1.0,539000,2000,709,760,5,SF
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+1,4.0,4.0,3760000,1894,3085,1219,49,SF
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+1,3.0,2.0,1650000,1922,1483,1113,106,SF
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data/seattle-weather.csv

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data/titanic.csv

@@ -0,0 +1,892 @@
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+1,0,3,"Braund, Mr. Owen Harris",male,22.0,1,0,A/5 21171,7.25,,S
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+3,1,3,"Heikkinen, Miss. Laina",female,26.0,0,0,STON/O2. 3101282,7.925,,S
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+6,0,3,"Moran, Mr. James",male,,0,0,330877,8.4583,,Q
+7,0,1,"McCarthy, Mr. Timothy J",male,54.0,0,0,17463,51.8625,E46,S
+8,0,3,"Palsson, Master. Gosta Leonard",male,2.0,3,1,349909,21.075,,S
+9,1,3,"Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)",female,27.0,0,2,347742,11.1333,,S
+10,1,2,"Nasser, Mrs. Nicholas (Adele Achem)",female,14.0,1,0,237736,30.0708,,C
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+15,0,3,"Vestrom, Miss. Hulda Amanda Adolfina",female,14.0,0,0,350406,7.8542,,S
+16,1,2,"Hewlett, Mrs. (Mary D Kingcome) ",female,55.0,0,0,248706,16.0,,S
+17,0,3,"Rice, Master. Eugene",male,2.0,4,1,382652,29.125,,Q
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+65,0,1,"Stewart, Mr. Albert A",male,,0,0,PC 17605,27.7208,,C
+66,1,3,"Moubarek, Master. Gerios",male,,1,1,2661,15.2458,,C
+67,1,2,"Nye, Mrs. (Elizabeth Ramell)",female,29.0,0,0,C.A. 29395,10.5,F33,S
+68,0,3,"Crease, Mr. Ernest James",male,19.0,0,0,S.P. 3464,8.1583,,S
+69,1,3,"Andersson, Miss. Erna Alexandra",female,17.0,4,2,3101281,7.925,,S
+70,0,3,"Kink, Mr. Vincenz",male,26.0,2,0,315151,8.6625,,S
+71,0,2,"Jenkin, Mr. Stephen Curnow",male,32.0,0,0,C.A. 33111,10.5,,S
+72,0,3,"Goodwin, Miss. Lillian Amy",female,16.0,5,2,CA 2144,46.9,,S
+73,0,2,"Hood, Mr. Ambrose Jr",male,21.0,0,0,S.O.C. 14879,73.5,,S
+74,0,3,"Chronopoulos, Mr. Apostolos",male,26.0,1,0,2680,14.4542,,C
+75,1,3,"Bing, Mr. Lee",male,32.0,0,0,1601,56.4958,,S
+76,0,3,"Moen, Mr. Sigurd Hansen",male,25.0,0,0,348123,7.65,F G73,S
+77,0,3,"Staneff, Mr. Ivan",male,,0,0,349208,7.8958,,S
+78,0,3,"Moutal, Mr. Rahamin Haim",male,,0,0,374746,8.05,,S
+79,1,2,"Caldwell, Master. Alden Gates",male,0.83,0,2,248738,29.0,,S
+80,1,3,"Dowdell, Miss. Elizabeth",female,30.0,0,0,364516,12.475,,S
+81,0,3,"Waelens, Mr. Achille",male,22.0,0,0,345767,9.0,,S
+82,1,3,"Sheerlinck, Mr. Jan Baptist",male,29.0,0,0,345779,9.5,,S
+83,1,3,"McDermott, Miss. Brigdet Delia",female,,0,0,330932,7.7875,,Q
+84,0,1,"Carrau, Mr. Francisco M",male,28.0,0,0,113059,47.1,,S
+85,1,2,"Ilett, Miss. Bertha",female,17.0,0,0,SO/C 14885,10.5,,S
+86,1,3,"Backstrom, Mrs. Karl Alfred (Maria Mathilda Gustafsson)",female,33.0,3,0,3101278,15.85,,S
+87,0,3,"Ford, Mr. William Neal",male,16.0,1,3,W./C. 6608,34.375,,S
+88,0,3,"Slocovski, Mr. Selman Francis",male,,0,0,SOTON/OQ 392086,8.05,,S
+89,1,1,"Fortune, Miss. Mabel Helen",female,23.0,3,2,19950,263.0,C23 C25 C27,S
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+91,0,3,"Christmann, Mr. Emil",male,29.0,0,0,343276,8.05,,S
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+97,0,1,"Goldschmidt, Mr. George B",male,71.0,0,0,PC 17754,34.6542,A5,C
+98,1,1,"Greenfield, Mr. William Bertram",male,23.0,0,1,PC 17759,63.3583,D10 D12,C
+99,1,2,"Doling, Mrs. John T (Ada Julia Bone)",female,34.0,0,1,231919,23.0,,S
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extras/create_model.py

@@ -0,0 +1,24 @@
+import pandas as pd
+import numpy as np
+import pickle
+
+from sklearn.model_selection import train_test_split
+from sklearn.linear_model import LogisticRegression
+
+
+df = pd.read_csv('../data/titanic.csv')
+
+df = pd.get_dummies(df, columns=['sex'], drop_first=True)
+
+selected_features = ['fare', 'sex_male']
+
+X = df[selected_features]
+
+y = df['survived']
+
+model = LogisticRegression()
+
+model.fit(X, y)
+
+pickle.dump(model, open('../models/model.pkl', 'wb') )
+

extras/exploring.ipynb

@@ -0,0 +1,546 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "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>in_sf</th>\n",
+       "      <th>beds</th>\n",
+       "      <th>bath</th>\n",
+       "      <th>price</th>\n",
+       "      <th>year_built</th>\n",
+       "      <th>sqft</th>\n",
+       "      <th>price_per_sqft</th>\n",
+       "      <th>elevation</th>\n",
+       "      <th>city</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>999000</td>\n",
+       "      <td>1960</td>\n",
+       "      <td>1000</td>\n",
+       "      <td>999</td>\n",
+       "      <td>10</td>\n",
+       "      <td>NY</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>2750000</td>\n",
+       "      <td>2006</td>\n",
+       "      <td>1418</td>\n",
+       "      <td>1939</td>\n",
+       "      <td>0</td>\n",
+       "      <td>NY</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>1350000</td>\n",
+       "      <td>1900</td>\n",
+       "      <td>2150</td>\n",
+       "      <td>628</td>\n",
+       "      <td>9</td>\n",
+       "      <td>NY</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>629000</td>\n",
+       "      <td>1903</td>\n",
+       "      <td>500</td>\n",
+       "      <td>1258</td>\n",
+       "      <td>9</td>\n",
+       "      <td>NY</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>439000</td>\n",
+       "      <td>1930</td>\n",
+       "      <td>500</td>\n",
+       "      <td>878</td>\n",
+       "      <td>10</td>\n",
+       "      <td>NY</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   in_sf  beds  bath    price  year_built  sqft  price_per_sqft  elevation  \\\n",
+       "0      0   2.0   1.0   999000        1960  1000             999         10   \n",
+       "1      0   2.0   2.0  2750000        2006  1418            1939          0   \n",
+       "2      0   2.0   2.0  1350000        1900  2150             628          9   \n",
+       "3      0   1.0   1.0   629000        1903   500            1258          9   \n",
+       "4      0   0.0   1.0   439000        1930   500             878         10   \n",
+       "\n",
+       "  city  \n",
+       "0   NY  \n",
+       "1   NY  \n",
+       "2   NY  \n",
+       "3   NY  \n",
+       "4   NY  "
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df = pd.read_csv('../data/ny-vs-sf-houses.csv')\n",
+    "df.head()\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([<Axes: title={'center': 'NY'}>, <Axes: title={'center': 'SF'}>],\n",
+       "      dtype=object)"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "df.hist('elevation', by='city')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import seaborn as sns\n",
+    "\n",
+    "fig = sns.histplot(df, x='elevation', hue='city', multiple='stack')\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "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>in_sf</th>\n",
+       "      <th>beds</th>\n",
+       "      <th>bath</th>\n",
+       "      <th>price</th>\n",
+       "      <th>year_built</th>\n",
+       "      <th>sqft</th>\n",
+       "      <th>price_per_sqft</th>\n",
+       "      <th>elevation</th>\n",
+       "      <th>city</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>999000</td>\n",
+       "      <td>1982</td>\n",
+       "      <td>784</td>\n",
+       "      <td>1274</td>\n",
+       "      <td>5</td>\n",
+       "      <td>NY</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>58</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>775000</td>\n",
+       "      <td>2009</td>\n",
+       "      <td>546</td>\n",
+       "      <td>1419</td>\n",
+       "      <td>6</td>\n",
+       "      <td>NY</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>44</th>\n",
+       "      <td>0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>3995000</td>\n",
+       "      <td>1906</td>\n",
+       "      <td>2400</td>\n",
+       "      <td>1665</td>\n",
+       "      <td>10</td>\n",
+       "      <td>NY</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>439</th>\n",
+       "      <td>1</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>849000</td>\n",
+       "      <td>1947</td>\n",
+       "      <td>1622</td>\n",
+       "      <td>523</td>\n",
+       "      <td>106</td>\n",
+       "      <td>SF</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>220</th>\n",
+       "      <td>0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>529000</td>\n",
+       "      <td>1986</td>\n",
+       "      <td>650</td>\n",
+       "      <td>814</td>\n",
+       "      <td>0</td>\n",
+       "      <td>NY</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     in_sf  beds  bath    price  year_built  sqft  price_per_sqft  elevation  \\\n",
+       "11       0   1.0   1.0   999000        1982   784            1274          5   \n",
+       "58       0   0.0   1.0   775000        2009   546            1419          6   \n",
+       "44       0   2.0   2.0  3995000        1906  2400            1665         10   \n",
+       "439      1   3.0   2.0   849000        1947  1622             523        106   \n",
+       "220      0   1.0   1.0   529000        1986   650             814          0   \n",
+       "\n",
+       "    city  \n",
+       "11    NY  \n",
+       "58    NY  \n",
+       "44    NY  \n",
+       "439   SF  \n",
+       "220   NY  "
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.groupby('city')['price'].mean().round(0)\n",
+    "\n",
+    "\n",
+    "df.sample(n=5)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "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>a</th>\n",
+       "      <th>b</th>\n",
+       "      <th>c</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>-0.288585</td>\n",
+       "      <td>-1.823887</td>\n",
+       "      <td>-0.930694</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>-2.020028</td>\n",
+       "      <td>0.322731</td>\n",
+       "      <td>1.634198</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>-0.551412</td>\n",
+       "      <td>0.966280</td>\n",
+       "      <td>0.689169</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>0.548931</td>\n",
+       "      <td>-0.416653</td>\n",
+       "      <td>0.088240</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>-1.180181</td>\n",
+       "      <td>-0.218380</td>\n",
+       "      <td>0.350026</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>0.984890</td>\n",
+       "      <td>-0.620657</td>\n",
+       "      <td>0.218497</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>-1.174211</td>\n",
+       "      <td>0.985980</td>\n",
+       "      <td>0.591793</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>-0.461553</td>\n",
+       "      <td>2.075740</td>\n",
+       "      <td>0.371126</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>-1.692228</td>\n",
+       "      <td>1.191046</td>\n",
+       "      <td>0.863126</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>-0.354696</td>\n",
+       "      <td>-0.853733</td>\n",
+       "      <td>1.799386</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>1.165250</td>\n",
+       "      <td>2.035038</td>\n",
+       "      <td>-0.953814</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>-0.899864</td>\n",
+       "      <td>-0.469766</td>\n",
+       "      <td>0.531577</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>-0.608545</td>\n",
+       "      <td>-0.576878</td>\n",
+       "      <td>0.674811</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>-1.193892</td>\n",
+       "      <td>-0.498491</td>\n",
+       "      <td>-2.542653</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>-1.729489</td>\n",
+       "      <td>0.637867</td>\n",
+       "      <td>-1.093362</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>-0.994024</td>\n",
+       "      <td>-0.714704</td>\n",
+       "      <td>-0.166540</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16</th>\n",
+       "      <td>0.098802</td>\n",
+       "      <td>1.637319</td>\n",
+       "      <td>0.922935</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17</th>\n",
+       "      <td>1.326211</td>\n",
+       "      <td>0.282339</td>\n",
+       "      <td>1.709445</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>18</th>\n",
+       "      <td>-0.955390</td>\n",
+       "      <td>1.292335</td>\n",
+       "      <td>-0.140798</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19</th>\n",
+       "      <td>0.019700</td>\n",
+       "      <td>-2.386171</td>\n",
+       "      <td>-0.452989</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "           a         b         c\n",
+       "0  -0.288585 -1.823887 -0.930694\n",
+       "1  -2.020028  0.322731  1.634198\n",
+       "2  -0.551412  0.966280  0.689169\n",
+       "3   0.548931 -0.416653  0.088240\n",
+       "4  -1.180181 -0.218380  0.350026\n",
+       "5   0.984890 -0.620657  0.218497\n",
+       "6  -1.174211  0.985980  0.591793\n",
+       "7  -0.461553  2.075740  0.371126\n",
+       "8  -1.692228  1.191046  0.863126\n",
+       "9  -0.354696 -0.853733  1.799386\n",
+       "10  1.165250  2.035038 -0.953814\n",
+       "11 -0.899864 -0.469766  0.531577\n",
+       "12 -0.608545 -0.576878  0.674811\n",
+       "13 -1.193892 -0.498491 -2.542653\n",
+       "14 -1.729489  0.637867 -1.093362\n",
+       "15 -0.994024 -0.714704 -0.166540\n",
+       "16  0.098802  1.637319  0.922935\n",
+       "17  1.326211  0.282339  1.709445\n",
+       "18 -0.955390  1.292335 -0.140798\n",
+       "19  0.019700 -2.386171 -0.452989"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "chart_data = pd.DataFrame(np.random.randn(20, 3), columns=[\"a\", \"b\", \"c\"])\n",
+    "chart_data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "base",
+   "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": "3.12.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

pages/Bio.py

@@ -0,0 +1,30 @@
+import streamlit as st
+import pandas as pd
+import numpy as np
+
+
+st.set_page_config(
+    page_title="Hello, Im Zack. It's nice to meet you",
+    page_icon="👋",
+)
+
+st.write("# Welcome to my portfolio 👋")
+
+col1, col2 = st.columns([1,1])
+
+with col1:
+    st.image('https://media-exp1.licdn.com/dms/image/C5603AQFHVJRtVAazbw/profile-displayphoto-shrink_800_800/0/1594756980450?e=2147483647&v=beta&t=IiEk7RhiY2zon4WFPFoK4OFTR6Vc31HFPR3lJ076J_8')
+
+
+with col2:
+    st.image('https://i1.sndcdn.com/avatars-000034142709-fv26gu-t500x500.jpg')
+
+
+important_links = '''
+[github.com/zd123](https://github.com/zd123),
+[Company Github](https://github.com/CUNYTechPrep/2022-fall-data-science) _as a main contributor_.,
+[IG](https://www.instagram.com/zd1233/),
+[Surfline](https://www.surfline.com/)
+'''
+
+st.markdown(important_links)

pages/DataFrame.py

@@ -0,0 +1,41 @@
+import streamlit as st
+import pandas as pd
+import numpy as np
+import plotly.express as px
+import json
+
+
+@st.cache_data
+def load_covid_data(fp):
+    print('Running load_covid_data...')
+
+    # read in the csv via the link
+    df = pd.read_csv(fp)
+
+    # Creating the National Average
+    national_average = df.groupby('date')['cases_avg_per_100k'].mean()
+
+    # turn that into a data frame
+    national_average = pd.DataFrame(national_average).reset_index()
+
+    # create a state column
+    national_average['state'] = 'National Average'
+
+    # add it to the main dataframe
+    df = pd.concat([df, national_average])
+
+    df['datetime'] = pd.to_datetime(df['date'])
+
+    return(df)
+
+
+# loading the data
+fp = 'https://raw.githubusercontent.com/nytimes/covid-19-data/master/rolling-averages/us-states.csv'
+
+df = load_covid_data(fp) 
+
+summary_stats = df.groupby('state')['cases_avg_per_100k'].agg(['mean', 'median', 'min', 'max', 'std'])
+
+st.dataframe(
+	summary_stats.style.highlight_max(axis=0, color='red'),
+	use_container_width=True)

pages/Filter_Charts.py

@@ -0,0 +1,72 @@
+import streamlit as st
+import pandas as pd
+import numpy as np
+import seaborn as sns
+import matplotlib.pyplot as plt
+
+
+############### PAGE SETUP ########################
+###################################################
+
+st.set_page_config(layout="wide")
+st.header("Smiple Charts")
+
+
+############ HELPER FUNCTIONS #####################
+###################################################
+
+@st.cache_data
+def load_data():
+    df = pd.read_csv('data/ny-vs-sf-houses.csv')
+    return df
+
+def display_data(n=5):
+    #Randomize the data
+    display_df = df.sample(n=n)
+    st.dataframe(display_df)
+
+def plot_streamlit_scatter_chart():
+    st.scatter_chart(df, x='price', y='elevation', color='city', size='sqft') 
+
+def plot_static_seaborn_chart():
+    fig, ax = plt.subplots(figsize = (50,30))
+    _ = sns.histplot(df, x='elevation', hue='city', multiple='stack')
+    st.pyplot(fig)
+
+
+################## PAGE LAYOUT  ###################
+###################################################
+
+df = load_data()
+col1, col2 = st.columns([1, 3])
+
+## COL1 WILL HOLD OUR FILTERS
+with col1:
+    ## FILTERS
+    selected_cities = st.multiselect(
+        'Select which states to compare.',
+        ['SF', 'NY'],
+        default=['SF', 'NY'] )
+
+    # Use selected cities to filter the dataframe
+    df = df[df['city'].isin(selected_cities)]
+
+    # Create a price filter
+    price_filter = st.slider(
+        "Filter for only prices above",
+        min_value = df.price.min(),
+        max_value = df.price.max(),
+        step=100000
+    )
+    # filter for only prices greater than price_filter value. 
+    df = df[df['price'] >= price_filter]
+
+
+## COL2 WILL HOLD OUR CHARTS 
+with col2:
+    display_data()
+    plot_streamlit_scatter_chart()
+    plot_static_seaborn_chart()
+
+
+

pages/Interactive_Charts.py

@@ -0,0 +1,151 @@
+import streamlit as st
+import pandas as pd
+import numpy as np
+import plotly.express as px
+import json
+import datetime
+
+
+st.set_page_config(layout="wide")
+
+
+st.header("Covid Dashboard")
+
+
+@st.cache_data
+def load_covid_data(fp):
+    print('Running load_covid_data...')
+
+    # read in the csv via the link
+    df = pd.read_csv(fp)
+
+    # Creating the National Average
+    national_average = df.groupby('date')['cases_avg_per_100k'].mean()
+
+    # turn that into a data frame
+    national_average = pd.DataFrame(national_average).reset_index()
+
+    # create a state column
+    national_average['state'] = 'National Average'
+
+    # add it to the main dataframe
+    df = pd.concat([df, national_average])
+
+    df['datetime'] = pd.to_datetime(df['date'])
+
+    return(df)
+
+
+# loading the data
+fp = 'https://raw.githubusercontent.com/nytimes/covid-19-data/master/rolling-averages/us-states.csv'
+df = load_covid_data(fp) 
+
+
+# -- Create two columns
+col1, col2 = st.columns( [1,4] )
+
+with col1:
+    st.write('Average number of cases per 100k compared to the national average.')
+
+
+with col2:
+    # create a list of all the state names
+    state_list = sorted(df['state'].unique())
+    
+    # create a mulit select button
+    selected_states = st.multiselect(
+        'Select which states to compare.',
+        state_list,
+        default=['National Average']
+        )
+
+
+    # for debugging
+    print(type(selected_states), selected_states)
+
+
+    # extract just the selected states
+    state_df = df[df['state'].isin(selected_states)].copy()
+
+
+    # create summary stats from selected_states dataframe
+    summary_stats = state_df.groupby('state')['cases_avg_per_100k'].agg(['mean', 'median', 'min', 'max', 'std'])
+
+    # This should work but doesn't     
+    summary_stats.index = summary_stats.index.rename('Avg Cases Per 100k')
+
+    # display the summary stats table
+    st.write('Avg Cases Per 100k')
+    st.dataframe(summary_stats)
+
+
+    # create line chart with just selected states
+    fig = px.line(state_df, 
+        x='date', 
+        y='cases_avg_per_100k', 
+        line_group='state', 
+        color='state',
+        title="Average # of cases per 100k people.",
+        labels={ "cases_avg_per_100k": "Cases per 100k"} 
+        )
+
+    # changes the background color
+    fig.update_layout({
+        'plot_bgcolor': 'rgba(0, 0, 0, 0)',
+        'paper_bgcolor': 'rgba(0, 0, 0, 0)',
+        })
+
+    # changing the grid axes
+    fig.update_xaxes(showgrid=False, gridwidth=1, gridcolor='Gray')
+    fig.update_yaxes(showgrid=True, gridwidth=1, gridcolor='Gray')
+
+    # display graph
+    st.plotly_chart(fig, use_container_width=True)
+
+
+
+    # create line chart of just last 90 days.
+    today = state_df['datetime'].max()
+    d = datetime.timedelta(days = 90)
+    last_90 = today - d
+
+    df_last_ninety_days =  state_df[state_df['datetime'] > last_90]
+    fig = px.line(df_last_ninety_days, 
+        x='date', 
+        y='cases_avg_per_100k', 
+        line_group='state', 
+        color='state',
+        title="Last 90 Days of data:  Average # of cases per 100k people.",
+        labels={ "cases_avg_per_100k": "Cases per 100k"} 
+        )
+    # changest the background color
+    fig.update_layout({
+        'plot_bgcolor': 'rgba(0, 0, 0, 0)',
+        'paper_bgcolor': 'rgba(0, 0, 0, 0)',
+        })
+    
+    # changing the grid axes
+    fig.update_xaxes(showgrid=False, gridwidth=1, gridcolor='Gray')
+    fig.update_yaxes(showgrid=True, gridwidth=1, gridcolor='Gray')    
+
+    # display the chart.
+    st.plotly_chart(fig, use_container_width=True)
+
+
+# inside column 1
+with col1:
+
+    # for each state in selected states
+    for state in selected_states:
+        if state != 'National Average':
+            avg = df[df['state'] == state]['cases_avg_per_100k'].mean()
+            avg = round(avg, 2)
+            national_average = df[df['state'] == 'National Average']['cases_avg_per_100k'].mean()
+            national_average = round(national_average, 2)
+            delta = avg-national_average
+            delta = round(delta, 2)
+            st.metric(label=state, value=avg, delta=delta, help='Average Cases Per 100k and the little number in green or red below is how it compares to the national average')
+        else:
+            national_average = df[df['state'] == 'National Average']['cases_avg_per_100k'].mean()
+            national_average = round(national_average, 2)
+            st.metric(label=state, value=national_average) #, delta=0.0, delta_color='off')

pages/ML_Model.py

@@ -0,0 +1,64 @@
+import streamlit as st
+import pandas as pd
+import pickle
+import plotly.express as px
+
+# Load the model you already created...
+model = pickle.load( open('models/model.pkl', 'rb') )
+
+
+# Begin user inputs
+
+# Get fare
+fare = st.slider('Select passenger fare', 0, 99)
+ 
+print(fare)
+
+# Get Sex
+sex_male = st.radio('Pick passenger sex', ['Male', 'Female'])
+
+
+# Button to start prediction, Once make_prediction button is clicked...
+make_prediction = st.button('Submit and make prediction.')
+
+print('make prediction is set to:', make_prediction)
+
+# Once make_prediction button is clicked... the code below will run. 
+if make_prediction == True:
+	if sex_male == 'Male':
+		sex_male_int = 1
+	else:
+		sex_male_int = 0
+
+	# put the variables into a list in the same order as the model expects them in.
+	to_predict = [fare, sex_male_int]
+
+	# make a prediction
+	prediction = model.predict( [to_predict] )
+
+	# get the predicted probability 
+	prediction_proba = model.predict_proba([to_predict])
+
+	# debugging help
+	print(prediction_proba)
+
+	# extract the predicted value. 
+	value = prediction[0]
+
+	# if it predicts zero, then make output 'Death' else 'Survived'
+	# also get the predicted probability 
+	if value == 0:
+		pred_output = 'Death'
+		pred_proba = prediction_proba[0][0].round(2) * 100
+	else:
+		pred_output = 'Survival'
+		pred_proba = prediction_proba[0][1].round(2) * 100
+
+	# Generate output text
+	output_text = '## Predicted a ' + '%' + '**%s chance of %s** \n\n based on the input of %s' % (pred_proba, pred_output, str(to_predict))
+
+	# Display the users input variables back to them.
+	st.markdown(output_text)
+	st.markdown('Fare='+str(fare))
+	st.markdown('Sex='+sex_male)
+	st.balloons()

pages/Map.py

@@ -0,0 +1,90 @@
+import streamlit as st
+import pandas as pd
+import numpy as np
+import plotly.express as px
+import json
+
+
+#https://github.com/nytimes/covid-19-data/tree/master/rolling-averages
+st.set_page_config(layout="wide")
+
+def strip_fips(input_string):
+    try:
+        return( input_string.split('-')[1] )
+    except:
+        return(00000)
+
+
+@st.cache_data
+def load_geos(fp):
+    print('Running load_geos function...')
+    with open(fp) as response:
+        counties = json.load(response)
+    return(counties)
+
+
+
+@st.cache_data
+def load_counties(fp):
+    print('Running load_counties function...')
+    df = pd.read_csv(fp)
+
+    # extracting the fips code
+    df['fips'] = df['geoid'].apply(strip_fips)
+
+    # converting to date-time cause date_input needs to be a datetime object
+    df['date'] = pd.to_datetime(df['date'])
+    return(df)
+
+
+
+
+
+county_fp = 'https://raw.githubusercontent.com/nytimes/covid-19-data/master/rolling-averages/us-counties-recent.csv'
+df_counties = load_counties(county_fp)
+
+geo_fp = 'data/geojson-counties-fips.json'
+counties = load_geos(geo_fp)
+
+
+# getting the valid dates
+max_date = df_counties.date.max()
+min_date = df_counties.date.min()
+
+
+# setting the default value to the max date
+# and limiting selection to only dates in dataframe
+selected_date = st.date_input(
+    "Select date to see daily covid rate", 
+    min_value=min_date,
+    max_value=max_date,
+    value=max_date)
+
+
+st.write("Displaying data for %s" % selected_date)
+
+# selecting just the selected date
+df_to_plot = df_counties[ df_counties['date'] == str(selected_date)].copy()
+
+
+# Creating the choropleth map
+fig = px.choropleth(df_to_plot, 
+                    geojson=counties, 
+                    locations='fips', 
+                    color='cases_avg_per_100k',
+                    color_continuous_scale="OrRd",
+                    scope="usa",
+                    range_color=(0, 50), 
+                    hover_data=["county", "state", "cases_avg_per_100k"]
+                   )
+
+
+# changing the charts background color
+fig.update_layout(geo=dict(bgcolor= 'rgba(0,0,0,0)'))
+fig.update_traces(marker_line_width=0)
+
+
+# pushing the chart to the page
+st.plotly_chart(fig, use_container_width=True)
+
+

pages/Simple_Charts.py

@@ -0,0 +1,55 @@
+import streamlit as st
+import pandas as pd
+import numpy as np
+import seaborn as sns
+import matplotlib.pyplot as plt
+
+
+############### PAGE SETUP ########################
+###################################################
+st.set_page_config(layout="wide")
+st.header("Smiple Charts")
+
+
+
+############ HELPER FUNCTIONS #####################
+###################################################
+@st.cache_data
+def load_data():
+    df = pd.read_csv('data/ny-vs-sf-houses.csv')
+    return df
+
+################# PAGE LAYOUT #####################
+###################################################
+
+# Load the data
+df = load_data()
+
+# Display the data
+display_df = df.sample(n=5)
+st.dataframe(display_df)
+
+# do a scatter chart
+st.scatter_chart(df, x='price', y='elevation', color='city', size='sqft') 
+
+col1, col2, col3 = st.columns(3)
+with col1:
+    # Make and new histo and display it
+    fig, ax = plt.subplots(figsize = (13,8))
+    _ = sns.histplot(df, x='elevation', hue='city', multiple='stack')
+    st.pyplot(fig)
+
+
+st.markdown('---')
+st.header('Weather Data')
+weather_df = pd.read_csv('data/seattle-weather.csv')
+weather_df_display = weather_df.sample(n=5)
+
+st.dataframe(weather_df_display)
+st.bar_chart(weather_df, y='weather')
+st.dataframe(weather_df.value_counts('weather'))
+st.bar_chart(weather_df, y='temp_max')
+
+
+
+

requirements.txt

@@ -0,0 +1,8 @@
+streamlit
+pandas
+numpy
+plotly
+plotly-express
+scikit-learn
+matplotlib
+seaborn