From bf925381289950637fffcd19e077927e5ddc4b59 Mon Sep 17 00:00:00 2001 From: x256 <153709773+xTwo56@users.noreply.github.com> Date: Mon, 15 Apr 2024 11:49:29 +0530 Subject: [PATCH] feat: correlation between features and target --- Accommodating_Insights.ipynb | 642 +++++++++++++++++++++++++++++------ 1 file changed, 536 insertions(+), 106 deletions(-) diff --git a/Accommodating_Insights.ipynb b/Accommodating_Insights.ipynb index fcbad54..9b9f6c1 100644 --- a/Accommodating_Insights.ipynb +++ b/Accommodating_Insights.ipynb @@ -64,19 +64,10 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 86, "id": "541a77f6", "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/usr/lib/python3/dist-packages/scipy/__init__.py:146: UserWarning: A NumPy version >=1.17.3 and <1.25.0 is required for this version of SciPy (detected version 1.26.4\n", - " warnings.warn(f\"A NumPy version >={np_minversion} and <{np_maxversion}\"\n" - ] - } - ], + "outputs": [], "source": [ "import numpy as np\n", "import pandas as pd \n", @@ -102,7 +93,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 87, "id": "8c34a82c", "metadata": {}, "outputs": [], @@ -112,7 +103,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 88, "id": "e8a971fd", "metadata": {}, "outputs": [ @@ -285,7 +276,7 @@ "4 0.10 1 0 " ] }, - "execution_count": 3, + "execution_count": 88, "metadata": {}, "output_type": "execute_result" } @@ -296,7 +287,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 89, "id": "6ee6552e", "metadata": {}, "outputs": [ @@ -336,7 +327,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 90, "id": "42680b10", "metadata": {}, "outputs": [ @@ -346,7 +337,7 @@ "(48895, 16)" ] }, - "execution_count": 5, + "execution_count": 90, "metadata": {}, "output_type": "execute_result" } @@ -367,7 +358,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 91, "id": "08b3b584", "metadata": {}, "outputs": [], @@ -379,7 +370,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 92, "id": "f6c2dbcd", "metadata": {}, "outputs": [], @@ -389,7 +380,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 93, "id": "3537dcad", "metadata": {}, "outputs": [ @@ -538,7 +529,7 @@ "4 0 " ] }, - "execution_count": 8, + "execution_count": 93, "metadata": {}, "output_type": "execute_result" } @@ -549,7 +540,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 94, "id": "eecf8db6", "metadata": {}, "outputs": [ @@ -559,7 +550,7 @@ "12" ] }, - "execution_count": 9, + "execution_count": 94, "metadata": {}, "output_type": "execute_result" } @@ -578,7 +569,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 95, "id": "a15d8148", "metadata": {}, "outputs": [ @@ -588,7 +579,7 @@ "(48895, 12)" ] }, - "execution_count": 10, + "execution_count": 95, "metadata": {}, "output_type": "execute_result" } @@ -599,7 +590,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 96, "id": "ebf8c82f", "metadata": {}, "outputs": [ @@ -621,7 +612,7 @@ "dtype: object" ] }, - "execution_count": 11, + "execution_count": 96, "metadata": {}, "output_type": "execute_result" } @@ -632,7 +623,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 97, "id": "ce271821", "metadata": {}, "outputs": [ @@ -781,7 +772,7 @@ "48894 1 23 " ] }, - "execution_count": 12, + "execution_count": 97, "metadata": {}, "output_type": "execute_result" } @@ -792,7 +783,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 98, "id": "5c3a9281", "metadata": {}, "outputs": [ @@ -952,7 +943,7 @@ "max 365.000000 " ] }, - "execution_count": 13, + "execution_count": 98, "metadata": {}, "output_type": "execute_result" } @@ -963,7 +954,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 99, "id": "ee7d7203", "metadata": {}, "outputs": [ @@ -985,7 +976,7 @@ "dtype: int64" ] }, - "execution_count": 14, + "execution_count": 99, "metadata": {}, "output_type": "execute_result" } @@ -1004,7 +995,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 100, "id": "71f8c6e5", "metadata": {}, "outputs": [ @@ -1014,7 +1005,7 @@ "Text(0.5, 1.0, 'Room Type v/s Price')" ] }, - "execution_count": 15, + "execution_count": 100, "metadata": {}, "output_type": "execute_result" }, @@ -1068,7 +1059,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 101, "id": "95fb19c2", "metadata": {}, "outputs": [ @@ -1078,7 +1069,7 @@ "Text(0.5, 1.0, 'Neighbourhood Group v/s Price')" ] }, - "execution_count": 16, + "execution_count": 101, "metadata": {}, "output_type": "execute_result" }, @@ -1123,7 +1114,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 102, "id": "c23d2200", "metadata": {}, "outputs": [ @@ -1133,7 +1124,7 @@ "Text(0.5, 1.0, 'Number of Reviews v/s Price')" ] }, - "execution_count": 17, + "execution_count": 102, "metadata": {}, "output_type": "execute_result" }, @@ -1162,7 +1153,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 103, "id": "2602e744", "metadata": {}, "outputs": [ @@ -1179,7 +1170,7 @@ "-0.04795422658266219" ] }, - "execution_count": 18, + "execution_count": 103, "metadata": {}, "output_type": "execute_result" } @@ -1208,7 +1199,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 104, "id": "76b37eee", "metadata": {}, "outputs": [ @@ -1226,7 +1217,7 @@ "Name: price, dtype: float64" ] }, - "execution_count": 19, + "execution_count": 104, "metadata": {}, "output_type": "execute_result" } @@ -1246,7 +1237,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 105, "id": "f436c55f", "metadata": {}, "outputs": [], @@ -1256,17 +1247,17 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 106, "id": "0cca79ea", "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "[]" + "[]" ] }, - "execution_count": 21, + "execution_count": 106, "metadata": {}, "output_type": "execute_result" }, @@ -1307,7 +1298,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 107, "id": "68b188a2", "metadata": {}, "outputs": [ @@ -1329,7 +1320,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 108, "id": "093a8ae8", "metadata": {}, "outputs": [], @@ -1340,7 +1331,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 109, "id": "e2fe39ad", "metadata": {}, "outputs": [], @@ -1351,7 +1342,7 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 110, "id": "4d8da7c9", "metadata": {}, "outputs": [], @@ -1363,7 +1354,7 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 111, "id": "ca1d5542", "metadata": {}, "outputs": [ @@ -1371,7 +1362,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/tmp/ipykernel_42243/535378691.py:2: FutureWarning: \n", + "/tmp/ipykernel_6879/535378691.py:2: FutureWarning: \n", "\n", "`shade` is now deprecated in favor of `fill`; setting `fill=True`.\n", "This will become an error in seaborn v0.14.0; please update your code.\n", @@ -1382,10 +1373,10 @@ { "data": { "text/plain": [ - "[]" + "[]" ] }, - "execution_count": 26, + "execution_count": 111, "metadata": {}, "output_type": "execute_result" }, @@ -1410,7 +1401,7 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 112, "id": "f182e7f8", "metadata": {}, "outputs": [ @@ -1441,7 +1432,7 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 113, "id": "29347701", "metadata": {}, "outputs": [], @@ -1452,7 +1443,7 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 114, "id": "413984b1", "metadata": {}, "outputs": [ @@ -1607,7 +1598,7 @@ "4 0 4.394449 " ] }, - "execution_count": 29, + "execution_count": 114, "metadata": {}, "output_type": "execute_result" } @@ -1618,7 +1609,7 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 115, "id": "b91889a2", "metadata": {}, "outputs": [], @@ -1636,7 +1627,7 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 116, "id": "7d706379", "metadata": {}, "outputs": [ @@ -1659,7 +1650,7 @@ "dtype: int64" ] }, - "execution_count": 31, + "execution_count": 116, "metadata": {}, "output_type": "execute_result" } @@ -1670,7 +1661,7 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": 117, "id": "67624bb2", "metadata": {}, "outputs": [ @@ -1692,7 +1683,7 @@ "dtype: int64" ] }, - "execution_count": 32, + "execution_count": 117, "metadata": {}, "output_type": "execute_result" } @@ -1719,7 +1710,7 @@ }, { "cell_type": "code", - "execution_count": 33, + "execution_count": 118, "id": "6b63d81a", "metadata": {}, "outputs": [ @@ -1729,7 +1720,7 @@ "Text(0.5, 1.0, 'Reviews per month v/s Price')" ] }, - "execution_count": 33, + "execution_count": 118, "metadata": {}, "output_type": "execute_result" }, @@ -1757,7 +1748,7 @@ }, { "cell_type": "code", - "execution_count": 34, + "execution_count": 119, "id": "68e8bfea", "metadata": {}, "outputs": [ @@ -1767,7 +1758,7 @@ "numpy.float64" ] }, - "execution_count": 34, + "execution_count": 119, "metadata": {}, "output_type": "execute_result" } @@ -1780,7 +1771,7 @@ }, { "cell_type": "code", - "execution_count": 35, + "execution_count": 120, "id": "7730aae1", "metadata": {}, "outputs": [], @@ -1792,7 +1783,7 @@ }, { "cell_type": "code", - "execution_count": 36, + "execution_count": 121, "id": "0e19d92d", "metadata": {}, "outputs": [ @@ -1802,7 +1793,7 @@ "Text(0.5, 1.0, 'Reviews per month v/s Price (after)')" ] }, - "execution_count": 36, + "execution_count": 121, "metadata": {}, "output_type": "execute_result" }, @@ -1851,7 +1842,7 @@ }, { "cell_type": "code", - "execution_count": 37, + "execution_count": 122, "id": "5a8a3389", "metadata": {}, "outputs": [ @@ -1911,7 +1902,7 @@ }, { "cell_type": "code", - "execution_count": 38, + "execution_count": 123, "id": "3dd14f34", "metadata": {}, "outputs": [ @@ -1937,7 +1928,7 @@ }, { "cell_type": "code", - "execution_count": 39, + "execution_count": 124, "id": "9dc69ff4", "metadata": {}, "outputs": [ @@ -2179,7 +2170,7 @@ }, { "cell_type": "code", - "execution_count": 40, + "execution_count": 125, "id": "31e5a104", "metadata": {}, "outputs": [ @@ -2203,7 +2194,7 @@ }, { "cell_type": "code", - "execution_count": 41, + "execution_count": 126, "id": "100054b4", "metadata": {}, "outputs": [ @@ -2260,7 +2251,7 @@ }, { "cell_type": "code", - "execution_count": 36, + "execution_count": 127, "id": "feae0c0d", "metadata": {}, "outputs": [], @@ -2296,7 +2287,7 @@ }, { "cell_type": "code", - "execution_count": 37, + "execution_count": 128, "id": "34332ed0", "metadata": {}, "outputs": [ @@ -2304,7 +2295,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "Observations: 48895\n", + "Observations: 48884\n", "Variables: 12\n", "cat_cols: 2\n", "num_cols: 10\n", @@ -2319,7 +2310,7 @@ }, { "cell_type": "code", - "execution_count": 38, + "execution_count": 129, "id": "bcb67ba6", "metadata": {}, "outputs": [ @@ -2329,7 +2320,7 @@ "['neighbourhood_group', 'room_type']" ] }, - "execution_count": 38, + "execution_count": 129, "metadata": {}, "output_type": "execute_result" } @@ -2340,7 +2331,7 @@ }, { "cell_type": "code", - "execution_count": 39, + "execution_count": 130, "id": "8f575a30", "metadata": {}, "outputs": [ @@ -2359,7 +2350,7 @@ " 'price_log']" ] }, - "execution_count": 39, + "execution_count": 130, "metadata": {}, "output_type": "execute_result" } @@ -2370,7 +2361,7 @@ }, { "cell_type": "code", - "execution_count": 40, + "execution_count": 131, "id": "d9b0d757", "metadata": {}, "outputs": [ @@ -2380,7 +2371,7 @@ "[]" ] }, - "execution_count": 40, + "execution_count": 131, "metadata": {}, "output_type": "execute_result" } @@ -2391,7 +2382,7 @@ }, { "cell_type": "code", - "execution_count": 41, + "execution_count": 132, "id": "9588915e", "metadata": {}, "outputs": [ @@ -2400,24 +2391,24 @@ "output_type": "stream", "text": [ "\n", - "RangeIndex: 48895 entries, 0 to 48894\n", + "Index: 48884 entries, 0 to 48894\n", "Data columns (total 12 columns):\n", " # Column Non-Null Count Dtype \n", "--- ------ -------------- ----- \n", - " 0 neighbourhood_group 48895 non-null int8 \n", - " 1 neighbourhood 48895 non-null int16 \n", - " 2 latitude 48895 non-null float64\n", - " 3 longitude 48895 non-null float64\n", - " 4 room_type 48895 non-null int8 \n", - " 5 price 48895 non-null int64 \n", - " 6 minimum_nights 48895 non-null int64 \n", - " 7 number_of_reviews 48895 non-null int64 \n", - " 8 reviews_per_month 48895 non-null float64\n", - " 9 calculated_host_listings_count 48895 non-null int64 \n", - " 10 availability_365 48895 non-null int64 \n", - " 11 price_log 48895 non-null float64\n", + " 0 neighbourhood_group 48884 non-null int8 \n", + " 1 neighbourhood 48884 non-null int16 \n", + " 2 latitude 48884 non-null float64\n", + " 3 longitude 48884 non-null float64\n", + " 4 room_type 48884 non-null int8 \n", + " 5 price 48884 non-null int64 \n", + " 6 minimum_nights 48884 non-null int64 \n", + " 7 number_of_reviews 48884 non-null int64 \n", + " 8 reviews_per_month 38833 non-null float64\n", + " 9 calculated_host_listings_count 48884 non-null int64 \n", + " 10 availability_365 48884 non-null int64 \n", + " 11 price_log 48884 non-null float64\n", "dtypes: float64(4), int16(1), int64(5), int8(2)\n", - "memory usage: 3.5 MB\n" + "memory usage: 3.9 MB\n" ] } ], @@ -2435,7 +2426,7 @@ }, { "cell_type": "code", - "execution_count": 102, + "execution_count": 133, "id": "cca66e2a", "metadata": {}, "outputs": [], @@ -2446,16 +2437,455 @@ }, { "cell_type": "code", - "execution_count": null, - "id": "e2ff991c", + "execution_count": 149, + "id": "f7025f89-2bf3-4cc7-b5be-c72f5f4eae55", "metadata": {}, - 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neighbourhood_groupneighbourhoodlatitudelongituderoom_typepriceminimum_nightsnumber_of_reviewsreviews_per_monthcalculated_host_listings_countavailability_365price_log
neighbourhood_group1.0000000.1111870.2795980.101364-0.0161040.0441230.0174090.0039390.0738920.0736660.0805060.079586
neighbourhood0.1111871.0000000.234743-0.102253-0.0716850.0619670.026023-0.038229-0.0492520.010326-0.0345810.138974
latitude0.2795980.2347431.0000000.0848300.0065990.0338990.024893-0.015357-0.0101170.019518-0.0109420.079285
longitude0.101364-0.1022530.0848301.0000000.184192-0.149954-0.0627720.0590150.145888-0.1147150.082669-0.325892
room_type-0.016104-0.0716850.0065990.1841921.000000-0.249288-0.0699090.0026710.040670-0.1060730.022381-0.612369
price0.0441230.0619670.033899-0.149954-0.2492881.0000000.042805-0.047926-0.0305750.0574620.0818470.640074
minimum_nights0.0174090.0260230.024893-0.062772-0.0699090.0428051.000000-0.080080-0.1216440.1279620.1442750.033386
number_of_reviews0.003939-0.038229-0.0153570.0590150.002671-0.047926-0.0800801.0000000.549763-0.0723850.171975-0.042650
reviews_per_month0.073892-0.049252-0.0101170.1458880.040670-0.030575-0.1216440.5497631.000000-0.0094310.185730-0.039354
calculated_host_listings_count0.0736660.0103260.019518-0.114715-0.1060730.0574620.127962-0.072385-0.0094311.0000000.2257120.132836
availability_3650.080506-0.034581-0.0109420.0826690.0223810.0818470.1442750.1719750.1857300.2257121.0000000.099179
price_log0.0795860.1389740.079285-0.325892-0.6123690.6400740.033386-0.042650-0.0393540.1328360.0991791.000000
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" + ], + "text/plain": [ + " neighbourhood_group neighbourhood latitude \\\n", + "neighbourhood_group 1.000000 0.111187 0.279598 \n", + "neighbourhood 0.111187 1.000000 0.234743 \n", + "latitude 0.279598 0.234743 1.000000 \n", + "longitude 0.101364 -0.102253 0.084830 \n", + "room_type -0.016104 -0.071685 0.006599 \n", + "price 0.044123 0.061967 0.033899 \n", + "minimum_nights 0.017409 0.026023 0.024893 \n", + "number_of_reviews 0.003939 -0.038229 -0.015357 \n", + "reviews_per_month 0.073892 -0.049252 -0.010117 \n", + "calculated_host_listings_count 0.073666 0.010326 0.019518 \n", + "availability_365 0.080506 -0.034581 -0.010942 \n", + "price_log 0.079586 0.138974 0.079285 \n", + "\n", + " longitude room_type price \\\n", + "neighbourhood_group 0.101364 -0.016104 0.044123 \n", + "neighbourhood -0.102253 -0.071685 0.061967 \n", + "latitude 0.084830 0.006599 0.033899 \n", + "longitude 1.000000 0.184192 -0.149954 \n", + "room_type 0.184192 1.000000 -0.249288 \n", + "price -0.149954 -0.249288 1.000000 \n", + "minimum_nights -0.062772 -0.069909 0.042805 \n", + "number_of_reviews 0.059015 0.002671 -0.047926 \n", + "reviews_per_month 0.145888 0.040670 -0.030575 \n", + "calculated_host_listings_count -0.114715 -0.106073 0.057462 \n", + "availability_365 0.082669 0.022381 0.081847 \n", + "price_log -0.325892 -0.612369 0.640074 \n", + "\n", + " minimum_nights number_of_reviews \\\n", + "neighbourhood_group 0.017409 0.003939 \n", + "neighbourhood 0.026023 -0.038229 \n", + "latitude 0.024893 -0.015357 \n", + "longitude -0.062772 0.059015 \n", + "room_type -0.069909 0.002671 \n", + "price 0.042805 -0.047926 \n", + "minimum_nights 1.000000 -0.080080 \n", + "number_of_reviews -0.080080 1.000000 \n", + "reviews_per_month -0.121644 0.549763 \n", + "calculated_host_listings_count 0.127962 -0.072385 \n", + "availability_365 0.144275 0.171975 \n", + "price_log 0.033386 -0.042650 \n", + "\n", + " reviews_per_month \\\n", + "neighbourhood_group 0.073892 \n", + "neighbourhood -0.049252 \n", + "latitude -0.010117 \n", + "longitude 0.145888 \n", + "room_type 0.040670 \n", + "price -0.030575 \n", + "minimum_nights -0.121644 \n", + "number_of_reviews 0.549763 \n", + "reviews_per_month 1.000000 \n", + "calculated_host_listings_count -0.009431 \n", + "availability_365 0.185730 \n", + "price_log -0.039354 \n", + "\n", + " calculated_host_listings_count \\\n", + "neighbourhood_group 0.073666 \n", + "neighbourhood 0.010326 \n", + "latitude 0.019518 \n", + "longitude -0.114715 \n", + "room_type -0.106073 \n", + "price 0.057462 \n", + "minimum_nights 0.127962 \n", + "number_of_reviews -0.072385 \n", + "reviews_per_month -0.009431 \n", + "calculated_host_listings_count 1.000000 \n", + "availability_365 0.225712 \n", + "price_log 0.132836 \n", + "\n", + " availability_365 price_log \n", + "neighbourhood_group 0.080506 0.079586 \n", + "neighbourhood -0.034581 0.138974 \n", + "latitude -0.010942 0.079285 \n", + "longitude 0.082669 -0.325892 \n", + "room_type 0.022381 -0.612369 \n", + "price 0.081847 0.640074 \n", + "minimum_nights 0.144275 0.033386 \n", + "number_of_reviews 0.171975 -0.042650 \n", + "reviews_per_month 0.185730 -0.039354 \n", + "calculated_host_listings_count 0.225712 0.132836 \n", + "availability_365 1.000000 0.099179 \n", + "price_log 0.099179 1.000000 " + ] + }, + "execution_count": 149, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "mask = np.triu(np.ones_like(dff.corr(), dtype=bool))\n", + "plt.figure(figsize=(12, 8))\n", + "sns.heatmap(dff.corr(), cmap='RdBu', mask=mask)\n", + "dff.corr()" + ] + }, + { + "cell_type": "markdown", + "id": "4f8c1d22-dd67-4be0-9737-fe36a107506f", + "metadata": {}, + "source": [ + "## Observation\n", + "- longitude is weakly-negatively correlated with price\n", + "- As neighbourhood, neighbourhood_group and room_type are categorical values. Pearson coefficient will not be effective to find out correlation.\n", + "- longitude affects the price of hotels whereas the location data columns are not much correlated which verifies the above 2nd point" + ] + }, + { + "cell_type": "code", + "execution_count": 137, + "id": "76814467-9664-4922-848f-4eaf0188a417", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "neighbourhood_group\n", + "Bronx 87.577064\n", + "Queens 99.517649\n", + "Staten Island 114.812332\n", + "Brooklyn 124.438915\n", + "Manhattan 196.884903\n", + "Name: price, dtype: float64" + ] + }, + "execution_count": 137, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "g_ng = df.groupby('neighbourhood_group')\n", + "g_ng['price'].mean().sort_values()" + ] + }, + { + "cell_type": "code", + "execution_count": 138, + "id": "272b1c72-adbf-451e-bdcd-16cdc944382a", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "neighbourhood\n", + "Bull's Head 47.333333\n", + "Hunts Point 50.500000\n", + "Tremont 51.545455\n", + "Soundview 53.466667\n", + "New Dorp 57.000000\n", + " ... \n", + "Riverdale 442.090909\n", + "Sea Gate 487.857143\n", + "Tribeca 490.638418\n", + "Woodrow 700.000000\n", + "Fort Wadsworth 800.000000\n", + "Name: price, Length: 221, dtype: float64" + ] + }, + "execution_count": 138, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "g_n = df.groupby('neighbourhood')\n", + "g_n['price'].mean().sort_values()" + ] + }, + { + "cell_type": "code", + "execution_count": 139, + "id": "4349708b-bf77-4a62-8473-6d44ebd748fe", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "room_type\n", + "Shared room 70.248705\n", + "Private room 89.809131\n", + "Entire home/apt 211.810918\n", + "Name: price, dtype: float64" + ] + }, + "execution_count": 139, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "g_r = df.groupby('room_type')\n", + "g_r['price'].mean().sort_values()" + ] + }, + { + "cell_type": "markdown", + "id": "5c9dfcfc-dd57-431f-bbfa-e409758ee312", + "metadata": {}, + "source": [ + "## Observation\n", + "- All three categorical variables are correlated with price\n", + " - neighbourhood_group and neighbourhood both represents location of hotels. Mean prices of hotels varies with\n", + " differnent location\n", + " - Entire room rented for hotels in general are significantly more expensive than shared rooms " + ] }, { "cell_type": "code", "execution_count": null, - "id": "ce8eab80", + "id": "d2d0654c-48a0-4b71-ba28-140ff2177e8b", "metadata": {}, "outputs": [], "source": []