From 8ad246928769b37bd6bc4270de84df0e43550fef Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Robert=20Kwieci=C5=84ski?= Date: Thu, 21 May 2020 16:20:12 +0200 Subject: [PATCH] Slides added --- P0. Data preparation.ipynb | 491 ++++++- P1. Baseline.ipynb | 115 +- P1. Introduction and baseline.pdf | Bin 0 -> 2701858 bytes P2. Evaluation.ipynb | 1186 ++++------------- P2. Evaluation.pdf | Bin 0 -> 3037466 bytes P3. k-nearest neighbours.ipynb | 380 +----- P3. k-nearest neighbours.pdf | Bin 0 -> 2772248 bytes ...mbeddings in high demensional spaces.ipynb | 12 +- P4. Matrix Factorization.ipynb | 814 ++++------- P4. Matrix Factorization.pdf | Bin 0 -> 2638726 bytes P5. Graph-based models.pdf | Bin 0 -> 2665695 bytes P5. Graph-based.ipynb | 453 ++++--- 12 files changed, 1390 insertions(+), 2061 deletions(-) create mode 100644 P1. Introduction and baseline.pdf create mode 100644 P2. Evaluation.pdf create mode 100644 P3. k-nearest neighbours.pdf create mode 100644 P4. Matrix Factorization.pdf create mode 100644 P5. Graph-based models.pdf diff --git a/P0. Data preparation.ipynb b/P0. Data preparation.ipynb index fb58504..8d96904 100644 --- a/P0. Data preparation.ipynb +++ b/P0. Data preparation.ipynb @@ -9,10 +9,14 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ + "# if you don't have some library installed try using pip or pip3 to install it - you can do it from the notebook\n", + "# example: !pip install tqdm\n", + "# also on labs it's better to use python3 kernel - ipython3 notebook\n", + "\n", "import pandas as pd\n", "import numpy as np\n", "import scipy.sparse as sparse\n", @@ -50,7 +54,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 6, "metadata": {}, "outputs": [ { @@ -129,7 +133,7 @@ "4 166 346 1 886397596" ] }, - "execution_count": 7, + "execution_count": 6, "metadata": {}, "output_type": "execute_result" } @@ -147,7 +151,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 7, "metadata": {}, "outputs": [ { @@ -176,14 +180,14 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 8, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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\n", + "image/png": 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\n", "text/plain": [ - "
" + "" ] }, "metadata": { @@ -482,10 +486,10 @@ "1 2 GoldenEye (1995) 01-Jan-1995 NaN \n", "2 3 Four Rooms (1995) 01-Jan-1995 NaN \n", "\n", - " 4 5 6 7 8 9 ... \\\n", - "0 http://us.imdb.com/M/title-exact?Toy%20Story%2... 0 0 0 1 1 ... \n", - "1 http://us.imdb.com/M/title-exact?GoldenEye%20(... 0 1 1 0 0 ... \n", - "2 http://us.imdb.com/M/title-exact?Four%20Rooms%... 0 0 0 0 0 ... \n", + " 4 5 6 7 8 9 ... \\\n", + "0 http://us.imdb.com/M/title-exact?Toy%20Story%2... 0 0 0 1 1 ... \n", + "1 http://us.imdb.com/M/title-exact?GoldenEye%20(... 0 1 1 0 0 ... \n", + "2 http://us.imdb.com/M/title-exact?Four%20Rooms%... 0 0 0 0 0 ... \n", "\n", " 14 15 16 17 18 19 20 21 22 23 \n", "0 0 0 0 0 0 0 0 0 0 0 \n", @@ -596,12 +600,312 @@ " Crime, Drama, Thriller\n", " \n", " \n", + " 5\n", + " 6\n", + " Shanghai Triad (Yao a yao yao dao waipo qiao) ...\n", + " Drama\n", + " \n", + " \n", + " 6\n", + " 7\n", + " Twelve Monkeys (1995)\n", + " Drama, Sci-Fi\n", + " \n", + " \n", + " 7\n", + " 8\n", + " Babe (1995)\n", + " Children's, Comedy, Drama\n", + " \n", + " \n", + " 8\n", + " 9\n", + " Dead Man Walking (1995)\n", + " Drama\n", + " \n", + " \n", + " 9\n", + " 10\n", + " Richard III (1995)\n", + " Drama, War\n", + " \n", + " \n", + " 10\n", + " 11\n", + " Seven (Se7en) (1995)\n", + " Crime, Thriller\n", + " \n", + " \n", + " 11\n", + " 12\n", + " Usual Suspects, The (1995)\n", + " Crime, Thriller\n", + " \n", + " \n", + " 12\n", + " 13\n", + " Mighty Aphrodite (1995)\n", + " Comedy\n", + " \n", + " \n", + " 13\n", + " 14\n", + " Postino, Il (1994)\n", + " Drama, Romance\n", + " \n", + " \n", + " 14\n", + " 15\n", + " Mr. Holland's Opus (1995)\n", + " Drama\n", + " \n", + " \n", + " 15\n", + " 16\n", + " French Twist (Gazon maudit) (1995)\n", + " Comedy, Romance\n", + " \n", + " \n", + " 16\n", + " 17\n", + " From Dusk Till Dawn (1996)\n", + " Action, Comedy, Crime, Horror, Thriller\n", + " \n", + " \n", + " 17\n", + " 18\n", + " White Balloon, The (1995)\n", + " Drama\n", + " \n", + " \n", + " 18\n", + " 19\n", + " Antonia's Line (1995)\n", + " Drama\n", + " \n", + " \n", + " 19\n", + " 20\n", + " Angels and Insects (1995)\n", + " Drama, Romance\n", + " \n", + " \n", + " 20\n", + " 21\n", + " Muppet Treasure Island (1996)\n", + " Action, Adventure, Comedy, Musical, Thriller\n", + " \n", + " \n", + " 21\n", + " 22\n", + " Braveheart (1995)\n", + " Action, Drama, War\n", + " \n", + " \n", + " 22\n", + " 23\n", + " Taxi Driver (1976)\n", + " Drama, Thriller\n", + " \n", + " \n", + " 23\n", + " 24\n", + " Rumble in the Bronx (1995)\n", + " Action, Adventure, Crime\n", + " \n", + " \n", + " 24\n", + " 25\n", + " Birdcage, The (1996)\n", + " Comedy\n", + " \n", + " \n", + " 25\n", + " 26\n", + " Brothers McMullen, The (1995)\n", + " Comedy\n", + " \n", + " \n", + " 26\n", + " 27\n", + " Bad Boys (1995)\n", + " Action\n", + " \n", + " \n", + " 27\n", + " 28\n", + " Apollo 13 (1995)\n", + " Action, Drama, Thriller\n", + " \n", + " \n", + " 28\n", + " 29\n", + " Batman Forever (1995)\n", + " Action, Adventure, Comedy, Crime\n", + " \n", + " \n", + " 29\n", + " 30\n", + " Belle de jour (1967)\n", + " Drama\n", + " \n", + " \n", " ...\n", " ...\n", " ...\n", " ...\n", " \n", " \n", + " 1652\n", + " 1653\n", + " Entertaining Angels: The Dorothy Day Story (1996)\n", + " Drama\n", + " \n", + " \n", + " 1653\n", + " 1654\n", + " Chairman of the Board (1998)\n", + " Comedy\n", + " \n", + " \n", + " 1654\n", + " 1655\n", + " Favor, The (1994)\n", + " Comedy, Romance\n", + " \n", + " \n", + " 1655\n", + " 1656\n", + " Little City (1998)\n", + " Comedy, Romance\n", + " \n", + " \n", + " 1656\n", + " 1657\n", + " Target (1995)\n", + " Action, Drama\n", + " \n", + " \n", + " 1657\n", + " 1658\n", + " Substance of Fire, The (1996)\n", + " Drama\n", + " \n", + " \n", + " 1658\n", + " 1659\n", + " Getting Away With Murder (1996)\n", + " Comedy\n", + " \n", + " \n", + " 1659\n", + " 1660\n", + " Small Faces (1995)\n", + " Drama\n", + " \n", + " \n", + " 1660\n", + " 1661\n", + " New Age, The (1994)\n", + " Drama\n", + " \n", + " \n", + " 1661\n", + " 1662\n", + " Rough Magic (1995)\n", + " Drama, Romance\n", + " \n", + " \n", + " 1662\n", + " 1663\n", + " Nothing Personal (1995)\n", + " Drama, War\n", + " \n", + " \n", + " 1663\n", + " 1664\n", + " 8 Heads in a Duffel Bag (1997)\n", + " Comedy\n", + " \n", + " \n", + " 1664\n", + " 1665\n", + " Brother's Kiss, A (1997)\n", + " Drama\n", + " \n", + " \n", + " 1665\n", + " 1666\n", + " Ripe (1996)\n", + " Drama\n", + " \n", + " \n", + " 1666\n", + " 1667\n", + " Next Step, The (1995)\n", + " Drama\n", + " \n", + " \n", + " 1667\n", + " 1668\n", + " Wedding Bell Blues (1996)\n", + " Comedy\n", + " \n", + " \n", + " 1668\n", + " 1669\n", + " MURDER and murder (1996)\n", + " Crime, Drama, Mystery\n", + " \n", + " \n", + " 1669\n", + " 1670\n", + " Tainted (1998)\n", + " Comedy, Thriller\n", + " \n", + " \n", + " 1670\n", + " 1671\n", + " Further Gesture, A (1996)\n", + " Drama\n", + " \n", + " \n", + " 1671\n", + " 1672\n", + " Kika (1993)\n", + " Drama\n", + " \n", + " \n", + " 1672\n", + " 1673\n", + " Mirage (1995)\n", + " Action, Thriller\n", + " \n", + " \n", + " 1673\n", + " 1674\n", + " Mamma Roma (1962)\n", + " Drama\n", + " \n", + " \n", + " 1674\n", + " 1675\n", + " Sunchaser, The (1996)\n", + " Drama\n", + " \n", + " \n", + " 1675\n", + " 1676\n", + " War at Home, The (1996)\n", + " Drama\n", + " \n", + " \n", + " 1676\n", + " 1677\n", + " Sweet Nothing (1995)\n", + " Drama\n", + " \n", + " \n", " 1677\n", " 1678\n", " Mat' i syn (1997)\n", @@ -637,31 +941,131 @@ "" ], "text/plain": [ - " id title \\\n", - "0 1 Toy Story (1995) \n", - "1 2 GoldenEye (1995) \n", - "2 3 Four Rooms (1995) \n", - "3 4 Get Shorty (1995) \n", - "4 5 Copycat (1995) \n", - "... ... ... \n", - "1677 1678 Mat' i syn (1997) \n", - "1678 1679 B. Monkey (1998) \n", - "1679 1680 Sliding Doors (1998) \n", - "1680 1681 You So Crazy (1994) \n", - "1681 1682 Scream of Stone (Schrei aus Stein) (1991) \n", + " id title \\\n", + "0 1 Toy Story (1995) \n", + "1 2 GoldenEye (1995) \n", + "2 3 Four Rooms (1995) \n", + "3 4 Get Shorty (1995) \n", + "4 5 Copycat (1995) \n", + "5 6 Shanghai Triad (Yao a yao yao dao waipo qiao) ... \n", + "6 7 Twelve Monkeys (1995) \n", + "7 8 Babe (1995) \n", + "8 9 Dead Man Walking (1995) \n", + "9 10 Richard III (1995) \n", + "10 11 Seven (Se7en) (1995) \n", + "11 12 Usual Suspects, The (1995) \n", + "12 13 Mighty Aphrodite (1995) \n", + "13 14 Postino, Il (1994) \n", + "14 15 Mr. Holland's Opus (1995) \n", + "15 16 French Twist (Gazon maudit) (1995) \n", + "16 17 From Dusk Till Dawn (1996) \n", + "17 18 White Balloon, The (1995) \n", + "18 19 Antonia's Line (1995) \n", + "19 20 Angels and Insects (1995) \n", + "20 21 Muppet Treasure Island (1996) \n", + "21 22 Braveheart (1995) \n", + "22 23 Taxi Driver (1976) \n", + "23 24 Rumble in the Bronx (1995) \n", + "24 25 Birdcage, The (1996) \n", + "25 26 Brothers McMullen, The (1995) \n", + "26 27 Bad Boys (1995) \n", + "27 28 Apollo 13 (1995) \n", + "28 29 Batman Forever (1995) \n", + "29 30 Belle de jour (1967) \n", + "... ... ... \n", + "1652 1653 Entertaining Angels: The Dorothy Day Story (1996) \n", + "1653 1654 Chairman of the Board (1998) \n", + "1654 1655 Favor, The (1994) \n", + "1655 1656 Little City (1998) \n", + "1656 1657 Target (1995) \n", + "1657 1658 Substance of Fire, The (1996) \n", + "1658 1659 Getting Away With Murder (1996) \n", + "1659 1660 Small Faces (1995) \n", + "1660 1661 New Age, The (1994) \n", + "1661 1662 Rough Magic (1995) \n", + "1662 1663 Nothing Personal (1995) \n", + "1663 1664 8 Heads in a Duffel Bag (1997) \n", + "1664 1665 Brother's Kiss, A (1997) \n", + "1665 1666 Ripe (1996) \n", + "1666 1667 Next Step, The (1995) \n", + "1667 1668 Wedding Bell Blues (1996) \n", + "1668 1669 MURDER and murder (1996) \n", + "1669 1670 Tainted (1998) \n", + "1670 1671 Further Gesture, A (1996) \n", + "1671 1672 Kika (1993) \n", + "1672 1673 Mirage (1995) \n", + "1673 1674 Mamma Roma (1962) \n", + "1674 1675 Sunchaser, The (1996) \n", + "1675 1676 War at Home, The (1996) \n", + "1676 1677 Sweet Nothing (1995) \n", + "1677 1678 Mat' i syn (1997) \n", + "1678 1679 B. Monkey (1998) \n", + "1679 1680 Sliding Doors (1998) \n", + "1680 1681 You So Crazy (1994) \n", + "1681 1682 Scream of Stone (Schrei aus Stein) (1991) \n", "\n", - " genres \n", - "0 Animation, Children's, Comedy \n", - "1 Action, Adventure, Thriller \n", - "2 Thriller \n", - "3 Action, Comedy, Drama \n", - "4 Crime, Drama, Thriller \n", - "... ... \n", - "1677 Drama \n", - "1678 Romance, Thriller \n", - "1679 Drama, Romance \n", - "1680 Comedy \n", - "1681 Drama \n", + " genres \n", + "0 Animation, Children's, Comedy \n", + "1 Action, Adventure, Thriller \n", + "2 Thriller \n", + "3 Action, Comedy, Drama \n", + "4 Crime, Drama, Thriller \n", + "5 Drama \n", + "6 Drama, Sci-Fi \n", + "7 Children's, Comedy, Drama \n", + "8 Drama \n", + "9 Drama, War \n", + "10 Crime, Thriller \n", + "11 Crime, Thriller \n", + "12 Comedy \n", + "13 Drama, Romance \n", + "14 Drama \n", + "15 Comedy, Romance \n", + "16 Action, Comedy, Crime, Horror, Thriller \n", + "17 Drama \n", + "18 Drama \n", + "19 Drama, Romance \n", + "20 Action, Adventure, Comedy, Musical, Thriller \n", + "21 Action, Drama, War \n", + "22 Drama, Thriller \n", + "23 Action, Adventure, Crime \n", + "24 Comedy \n", + "25 Comedy \n", + "26 Action \n", + "27 Action, Drama, Thriller \n", + "28 Action, Adventure, Comedy, Crime \n", + "29 Drama \n", + "... ... \n", + "1652 Drama \n", + "1653 Comedy \n", + "1654 Comedy, Romance \n", + "1655 Comedy, Romance \n", + "1656 Action, Drama \n", + "1657 Drama \n", + "1658 Comedy \n", + "1659 Drama \n", + "1660 Drama \n", + "1661 Drama, Romance \n", + "1662 Drama, War \n", + "1663 Comedy \n", + "1664 Drama \n", + "1665 Drama \n", + "1666 Drama \n", + "1667 Comedy \n", + "1668 Crime, Drama, Mystery \n", + "1669 Comedy, Thriller \n", + "1670 Drama \n", + "1671 Drama \n", + "1672 Action, Thriller \n", + "1673 Drama \n", + "1674 Drama \n", + "1675 Drama \n", + "1676 Drama \n", + "1677 Drama \n", + "1678 Romance, Thriller \n", + "1679 Drama, Romance \n", + "1680 Comedy \n", + "1681 Drama \n", "\n", "[1682 rows x 3 columns]" ] @@ -685,7 +1089,18 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 25, + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "if not os.path.exists('./Datasets/toy-example/'):\n", + " os.mkdir('./Datasets/toy-example/')" + ] + }, + { + "cell_type": "code", + "execution_count": 24, "metadata": {}, "outputs": [], "source": [ diff --git a/P1. Baseline.ipynb b/P1. Baseline.ipynb index c5a0c3e..bacfb2d 100644 --- a/P1. Baseline.ipynb +++ b/P1. Baseline.ipynb @@ -9,7 +9,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ @@ -26,7 +26,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -43,7 +43,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 3, "metadata": {}, "outputs": [ { @@ -134,7 +134,7 @@ "4 560 24 2 879976772 559 23" ] }, - "execution_count": 4, + "execution_count": 3, "metadata": {}, "output_type": "execute_result" } @@ -145,7 +145,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 4, "metadata": {}, "outputs": [], "source": [ @@ -155,7 +155,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 5, "metadata": {}, "outputs": [], "source": [ @@ -169,7 +169,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 6, "metadata": {}, "outputs": [], "source": [ @@ -189,17 +189,17 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "<3x4 sparse matrix of type ''\n", + "<3x4 sparse matrix of type ''\n", "\twith 8 stored elements in Compressed Sparse Row format>" ] }, - "execution_count": 9, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" } @@ -214,7 +214,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 8, "metadata": {}, "outputs": [ { @@ -257,7 +257,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 9, "metadata": {}, "outputs": [ { @@ -283,7 +283,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 10, "metadata": {}, "outputs": [ { @@ -307,7 +307,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "563 ns ± 16.7 ns per loop (mean ± std. dev. of 7 runs, 1000000 loops each)\n", + "472 ns ± 7.02 ns per loop (mean ± std. dev. of 7 runs, 1000000 loops each)\n", "Inefficient way to access items rated by user:\n" ] }, @@ -325,7 +325,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "70.8 µs ± 2.93 µs per loop (mean ± std. dev. of 7 runs, 10000 loops each)\n" + "57.3 µs ± 6.26 µs per loop (mean ± std. dev. of 7 runs, 10000 loops each)\n" ] } ], @@ -350,7 +350,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 11, "metadata": {}, "outputs": [ { @@ -384,7 +384,7 @@ "matrix([[ 8, 3, 11]])" ] }, - "execution_count": 17, + "execution_count": 11, "metadata": {}, "output_type": "execute_result" } @@ -398,7 +398,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 12, "metadata": {}, "outputs": [ { @@ -411,7 +411,7 @@ { "data": { "text/plain": [ - "array([2.66666667, 1.5 , 3.66666667])" + "array([ 2.66666667, 1.5 , 3.66666667])" ] }, "metadata": {}, @@ -427,9 +427,9 @@ { "data": { "text/plain": [ - "matrix([[2.66666667, 0. , 0. ],\n", - " [0. , 1.5 , 0. ],\n", - " [0. , 0. , 3.66666667]])" + "matrix([[ 2.66666667, 0. , 0. ],\n", + " [ 0. , 1.5 , 0. ],\n", + " [ 0. , 0. , 3.66666667]])" ] }, "metadata": {}, @@ -445,9 +445,9 @@ { "data": { "text/plain": [ - "matrix([[2.66666667, 2.66666667, 2.66666667, 0. ],\n", - " [0. , 1.5 , 0. , 1.5 ],\n", - " [3.66666667, 0. , 3.66666667, 3.66666667]])" + "matrix([[ 2.66666667, 2.66666667, 2.66666667, 0. ],\n", + " [ 0. , 1.5 , 0. , 1.5 ],\n", + " [ 3.66666667, 0. , 3.66666667, 3.66666667]])" ] }, "metadata": {}, @@ -468,7 +468,7 @@ " [-1.66666667, 0. , 1.33333333, 0.33333333]])" ] }, - "execution_count": 19, + "execution_count": 12, "metadata": {}, "output_type": "execute_result" } @@ -498,7 +498,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 13, "metadata": {}, "outputs": [ { @@ -556,7 +556,20 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 21, + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "if not os.path.exists('./Recommendations generated/'):\n", + " os.mkdir('./Recommendations generated/')\n", + " os.mkdir('./Recommendations generated/ml-100k/')\n", + " os.mkdir('./Recommendations generated/toy-example/')" + ] + }, + { + "cell_type": "code", + "execution_count": 22, "metadata": {}, "outputs": [], "source": [ @@ -605,7 +618,7 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 23, "metadata": {}, "outputs": [], "source": [ @@ -642,7 +655,7 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 24, "metadata": {}, "outputs": [ { @@ -744,18 +757,18 @@ "" ], "text/plain": [ - " 0 1 2 3 4 5 6 7 8 9 ... 11 \\\n", - "0 1 5 3.529975 10 3.529975 25 3.529975 32 3.529975 33 ... 44 \n", - "1 2 1 3.529975 2 3.529975 3 3.529975 4 3.529975 5 ... 6 \n", + " 0 1 2 3 4 5 6 7 8 9 ... \\\n", + "0 1 5 3.529975 10 3.529975 25 3.529975 32 3.529975 33 ... \n", + "1 2 1 3.529975 2 3.529975 3 3.529975 4 3.529975 5 ... \n", "\n", - " 12 13 14 15 16 17 18 19 20 \n", - "0 3.529975 46 3.529975 50 3.529975 52 3.529975 55 3.529975 \n", - "1 3.529975 7 3.529975 8 3.529975 9 3.529975 11 3.529975 \n", + " 11 12 13 14 15 16 17 18 19 20 \n", + "0 44 3.529975 46 3.529975 50 3.529975 52 3.529975 55 3.529975 \n", + "1 6 3.529975 7 3.529975 8 3.529975 9 3.529975 11 3.529975 \n", "\n", "[2 rows x 21 columns]" ] }, - "execution_count": 31, + "execution_count": 24, "metadata": {}, "output_type": "execute_result" } @@ -773,7 +786,7 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": 25, "metadata": {}, "outputs": [], "source": [ @@ -792,7 +805,7 @@ }, { "cell_type": "code", - "execution_count": 33, + "execution_count": 26, "metadata": {}, "outputs": [], "source": [ @@ -865,7 +878,7 @@ }, { "cell_type": "code", - "execution_count": 41, + "execution_count": 27, "metadata": {}, "outputs": [ { @@ -1033,7 +1046,7 @@ }, { "cell_type": "code", - "execution_count": 36, + "execution_count": 28, "metadata": {}, "outputs": [], "source": [ @@ -1057,7 +1070,7 @@ }, { "cell_type": "code", - "execution_count": 37, + "execution_count": 29, "metadata": {}, "outputs": [], "source": [ @@ -1076,7 +1089,7 @@ }, { "cell_type": "code", - "execution_count": 46, + "execution_count": 30, "metadata": {}, "outputs": [ { @@ -1133,7 +1146,7 @@ }, { "cell_type": "code", - "execution_count": 47, + "execution_count": 31, "metadata": {}, "outputs": [ { @@ -1147,10 +1160,10 @@ { "data": { "text/plain": [ - "0.7524871012820799" + "0.75248710128207985" ] }, - "execution_count": 47, + "execution_count": 31, "metadata": {}, "output_type": "execute_result" } @@ -1180,24 +1193,24 @@ }, { "cell_type": "code", - "execution_count": 48, + "execution_count": 32, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "RMSE: 1.5186\n", - "MAE: 1.2188\n" + "RMSE: 1.5190\n", + "MAE: 1.2222\n" ] }, { "data": { "text/plain": [ - "1.2187837474576546" + "1.222159317007133" ] }, - "execution_count": 48, + "execution_count": 32, "metadata": {}, "output_type": "execute_result" } diff --git a/P1. Introduction and baseline.pdf b/P1. 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