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kaggle-notebooks-outputs-filtered-26

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{ "cells": [ { "cell_type": "code", "execution_count": null, "metadata": { "_cell_guid": "e5f23b83-28b3-e630-fe24-c12f2244ece6" }, "outputs": [], "source": [ "# This Python 3 environment comes with many helpful analytics libraries installed\n", "# It is ...
0001/164/1164387.ipynb
s3://data-agents/kaggle-outputs/sharded/017_00001.jsonl.gz
{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "_cell_guid": "8c9fb61a-38e5-b9ca-e097-6a3a0e77c32b" }, "outputs": [], "source": [ "import pandas as pd\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "%matplotlib inline" ] }, { ...
0001/164/1164389.ipynb
s3://data-agents/kaggle-outputs/sharded/017_00001.jsonl.gz
{ "cells": [ { "cell_type": "markdown", "metadata": { "_cell_guid": "bb4a10c7-ea2b-e770-8043-be6638674ba2" }, "source": [] }, { "cell_type": "code", "execution_count": 1, "metadata": { "_cell_guid": "a9d61164-8bef-9611-4009-dbe0de2dd285" }, "outputs": [], "source": [ "# ...
0001/164/1164419.ipynb
s3://data-agents/kaggle-outputs/sharded/017_00001.jsonl.gz
{ "cells": [ { "cell_type": "markdown", "metadata": { "_cell_guid": "07056eb3-13bf-ea89-0bf4-eff25af76c7e" }, "source": [] }, { "cell_type": "code", "execution_count": 1, "metadata": { "_cell_guid": "adc6818d-ec08-977f-ba2e-a249c15ba439" }, "outputs": [ { "name": "stdo...
0001/164/1164428.ipynb
s3://data-agents/kaggle-outputs/sharded/017_00001.jsonl.gz
{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "_cell_guid": "5f888066-4ffd-9270-5d99-b746f80e36c0" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "macro.csv\n", "sample_submission.csv\n", "test.csv\n", "tr...
0001/164/1164545.ipynb
s3://data-agents/kaggle-outputs/sharded/017_00001.jsonl.gz
{ "cells": [ { "cell_type": "markdown", "metadata": { "_cell_guid": "1069ca7b-6e93-d353-a32e-c2683cb78a8c" }, "source": [] }, { "cell_type": "code", "execution_count": 1, "metadata": { "_cell_guid": "558c6a50-8363-c14e-45a4-6a5bab495642" }, "outputs": [ { "name": "stdo...
0001/164/1164595.ipynb
s3://data-agents/kaggle-outputs/sharded/017_00001.jsonl.gz
{ "cells": [ { "cell_type": "markdown", "metadata": { "_cell_guid": "3bf1b9f1-d2d4-49ea-c8a7-9ff4d80e4dae" }, "source": [ "Initial setup for understanding the data sources and merging them into one Pandas DataFrame." ] }, { "cell_type": "code", "execution_count": 1, "metadata": { ...
0001/164/1164737.ipynb
s3://data-agents/kaggle-outputs/sharded/017_00001.jsonl.gz
{ "cells": [ { "cell_type": "markdown", "metadata": { "_cell_guid": "3740a841-3ae6-e1c5-e09c-20c8cf305b02" }, "source": [] }, { "cell_type": "code", "execution_count": 1, "metadata": { "_cell_guid": "13914f63-f825-c319-346e-f7dca1aef15f" }, "outputs": [], "source": [ "im...
0001/164/1164806.ipynb
s3://data-agents/kaggle-outputs/sharded/017_00001.jsonl.gz
{ "cells": [ { "cell_type": "markdown", "metadata": { "_cell_guid": "77088dc8-35c4-97b4-bfd6-c1e10039956a" }, "source": [ "Titanic : Machine Learning from Disaster" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "_cell_guid": "10e91537-3719-3e97-add0-6fc98c9b8...
0001/164/1164819.ipynb
s3://data-agents/kaggle-outputs/sharded/017_00001.jsonl.gz
{ "cells": [ { "cell_type": "markdown", "metadata": { "_cell_guid": "9a45f08b-c554-1b35-a50d-c64150d6f789" }, "source": [ "In this notebook, we will try and explore the basic information about the dataset given.\n", "\n", "The dataset for this competition is...
0001/164/1164847.ipynb
s3://data-agents/kaggle-outputs/sharded/017_00001.jsonl.gz
{ "cells": [ { "cell_type": "markdown", "metadata": { "_cell_guid": "9a5d6243-2822-c6e6-e901-dc6ebd823f29" }, "source": [ "First, read in the data." ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "_cell_guid": "29ef415f-fcc3-1462-cb4b-f818cf9fe8b6" }, "o...
0001/164/1164851.ipynb
s3://data-agents/kaggle-outputs/sharded/017_00001.jsonl.gz
{ "cells": [ { "cell_type": "markdown", "metadata": { "_cell_guid": "f82893da-1f33-1a84-cc79-b07d70de91b5" }, "source": [ " who kills who? this is a interesting thing，then i will try to find the answer and visual them" ] }, { "cell_type": "code", "execution_count": 1, "metadata": ...
0001/164/1164881.ipynb
s3://data-agents/kaggle-outputs/sharded/017_00001.jsonl.gz
{ "cells": [ { "cell_type": "markdown", "metadata": { "_cell_guid": "77088dc8-35c4-97b4-bfd6-c1e10039956a" }, "source": [ "Titanic : Machine Learning from Disaster" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "_cell_guid": "10e91537-3719-3e97-add0-6fc98c9b8...
0001/165/1165025.ipynb
s3://data-agents/kaggle-outputs/sharded/017_00001.jsonl.gz
{ "cells": [ { "cell_type": "markdown", "metadata": { "_cell_guid": "1069ca7b-6e93-d353-a32e-c2683cb78a8c" }, "source": [] }, { "cell_type": "code", "execution_count": 1, "metadata": { "_cell_guid": "558c6a50-8363-c14e-45a4-6a5bab495642" }, "outputs": [ { "name": "stdo...
0001/165/1165049.ipynb
s3://data-agents/kaggle-outputs/sharded/017_00001.jsonl.gz
{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "_cell_guid": "1db51d00-dfb9-bde9-c89d-8532c0ee3b8f" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "frame_level\n", "label_names.csv\n", "sample_submission.csv\n", ...
0001/165/1165052.ipynb
s3://data-agents/kaggle-outputs/sharded/017_00001.jsonl.gz
{ "cells": [ { "cell_type": "markdown", "metadata": { "_cell_guid": "ae6fcdb9-25ab-9e72-9a92-0b402db82017" }, "source": [ "none" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "_cell_guid": "e87741db-88cd-b16a-90ef-b87e0d10e0e3" }, "outputs": [ { ...
0001/165/1165098.ipynb
s3://data-agents/kaggle-outputs/sharded/017_00001.jsonl.gz
{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "_cell_guid": "67cdb790-0ce8-c9cf-1007-18587572cba2" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "MarathonData.csv\n", "\n" ] } ], "source": [ "# Thi...
0001/165/1165167.ipynb
s3://data-agents/kaggle-outputs/sharded/017_00001.jsonl.gz
{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "_cell_guid": "f8c54601-2f9f-c179-9e44-a0b92ddc06ea" }, "outputs": [], "source": [ "from pandas import read_json\n", "data = read_json(\"../input/roam_prescription_based_prediction.jsonl\", lines=True)" ] }, ...
0001/165/1165175.ipynb
s3://data-agents/kaggle-outputs/sharded/017_00001.jsonl.gz
{ "cells": [ { "cell_type": "markdown", "metadata": { "_cell_guid": "22884160-55fc-bc8f-71f7-11f1976c719b" }, "source": [ "# Data Preparation & Feature Classification\n", "\n", "\n", "----------\n" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "_c...
0001/165/1165206.ipynb
s3://data-agents/kaggle-outputs/sharded/017_00001.jsonl.gz
{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "_cell_guid": "81dc4a9e-c22f-3ac2-6a05-92ebc090ab68" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(38133, 303)\n" ] } ], "source": [ "import numpy as np\n"...
0001/165/1165249.ipynb
s3://data-agents/kaggle-outputs/sharded/017_00001.jsonl.gz
{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "_cell_guid": "3fe9ea75-8a29-fc30-a5f5-ad5717f88123" }, "outputs": [ { "data": { "text/plain": [ "array([[ 1.33630621, -1.40451644, 1.29110641, -0.86687558],\n", " [-1.06904497, 0.84543708,...
0001/165/1165291.ipynb
s3://data-agents/kaggle-outputs/sharded/017_00001.jsonl.gz
{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "_cell_guid": "12813b60-b832-b436-2250-49c1f86954ed" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "genderclassmodel.csv\n", "gendermodel.csv\n", "gendermodel.py\n"...
0001/165/1165306.ipynb
s3://data-agents/kaggle-outputs/sharded/017_00001.jsonl.gz
{ "cells": [ { "cell_type": "markdown", "metadata": { "_cell_guid": "8ceaf8a7-7de6-620f-033b-b32330bf9c28" }, "source": [ "# The Monopoly of Olympic Scores" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "_cell_guid": "da799f41-4db3-6840-ccbf-8a274f5deb36" ...
0001/165/1165324.ipynb
s3://data-agents/kaggle-outputs/sharded/017_00001.jsonl.gz
{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "_cell_guid": "53cf0e54-0eef-23e5-fac3-f8d01aa615a5" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "genderclassmodel.csv\n", "gendermodel.csv\n", "gendermodel.py\n"...
0001/165/1165338.ipynb
s3://data-agents/kaggle-outputs/sharded/017_00001.jsonl.gz
{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "_cell_guid": "b631ff0a-b1dd-8786-ca3e-af5a49ec231d" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(42000, 784) (42000, 1)\n" ] } ], "source": [ "# This Pyt...
0001/165/1165377.ipynb
s3://data-agents/kaggle-outputs/sharded/017_00001.jsonl.gz
{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "_cell_guid": "49753661-46ec-9519-ae01-d5f284467006" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "database.sqlite\n", "\n" ] } ], "source": [ "# This...
0001/165/1165559.ipynb
s3://data-agents/kaggle-outputs/sharded/017_00001.jsonl.gz
{ "cells": [ { "cell_type": "markdown", "metadata": { "_cell_guid": "42d20a5d-5d9a-c1fe-ae2b-82ba349bd244" }, "source": [] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "5affaa39-72ed-fa0c-2764-2d13ad576d67" }, "source": [] }, { "cell_type": "markdown", "meta...
0001/165/1165571.ipynb
s3://data-agents/kaggle-outputs/sharded/017_00001.jsonl.gz
{ "cells": [ { "cell_type": "markdown", "metadata": { "_cell_guid": "42d20a5d-5d9a-c1fe-ae2b-82ba349bd244" }, "source": [ "## Introduction\n", "\n", "Within many sports, the youth stages of participation are often organized into annual age-groups using specific cutoff dates. Although the ...
0001/165/1165595.ipynb
s3://data-agents/kaggle-outputs/sharded/017_00001.jsonl.gz
{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "_cell_guid": "cc6aa7e1-7e45-13a1-4c05-442e168c817e" }, "outputs": [ { "data": { "text/html": "<script>requirejs.config({paths: { 'plotly': ['https://cdn.plot.ly/plotly-lates...
0001/165/1165602.ipynb
s3://data-agents/kaggle-outputs/sharded/017_00001.jsonl.gz
{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "_cell_guid": "04b4720d-8cba-7e72-ec30-6f60aac23984" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "numpy version: 1.12.1, pandas version: 0.19.2\n" ] } ], "sour...
0001/165/1165784.ipynb
s3://data-agents/kaggle-outputs/sharded/017_00001.jsonl.gz
{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "_cell_guid": "50cfce73-0cd8-092a-fe57-9c3ac000e875" }, "outputs": [], "source": [ "import pandas as pd\n", "import numpy as np\n", "\n", "# visualization\n", "import matplotlib.pyplot as plt\n", "%m...
0001/165/1165961.ipynb
s3://data-agents/kaggle-outputs/sharded/017_00001.jsonl.gz
{ "cells": [ { "cell_type": "markdown", "metadata": { "_cell_guid": "bc44aadf-8f87-5a40-b5a3-a9fb95f0dc56" }, "source": [ "As the title bears, I will show you complementary goods using nltk.\n", "## complementary goods from wiki ##\n", "> In economics, a complementary good or complement i...
0001/165/1165993.ipynb
s3://data-agents/kaggle-outputs/sharded/017_00001.jsonl.gz
{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "_cell_guid": "85873664-896c-f129-ec5f-b46ec2d580b6" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Dataset URL: ../input/Iris.csv\n" ] } ], "source": [ "# ...
0001/166/1166005.ipynb
s3://data-agents/kaggle-outputs/sharded/017_00001.jsonl.gz
{ "cells": [ { "cell_type": "markdown", "metadata": { "_cell_guid": "bc44aadf-8f87-5a40-b5a3-a9fb95f0dc56" }, "source": [ "As the title bears, I will show you complementary goods using nltk.\n", "\n", "[complementary goods (wiki)][1]\n", "\n", "\n", " [1]: https://en.wikipedi...
0001/166/1166061.ipynb
s3://data-agents/kaggle-outputs/sharded/017_00001.jsonl.gz
{ "cells": [ { "cell_type": "markdown", "metadata": { "_cell_guid": "c2d16684-51f1-45c3-f15d-24bc4bc2136f" }, "source": [] }, { "cell_type": "code", "execution_count": 1, "metadata": { "_cell_guid": "a0d10979-bac7-4a8f-89d6-535ff44f54dc" }, "outputs": [ { "name": "stdo...
0001/166/1166084.ipynb
s3://data-agents/kaggle-outputs/sharded/017_00001.jsonl.gz
{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "_cell_guid": "c8b7047a-c84c-c0ee-054d-a0c30609cc43" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Iris.csv\n", "database.sqlite\n", "\n" ] } ], "so...
0001/166/1166097.ipynb
s3://data-agents/kaggle-outputs/sharded/017_00001.jsonl.gz
{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "_cell_guid": "60b23005-d2b0-9953-4001-ef0c70a77148" }, "outputs": [], "source": [ "import numpy as np\n", "import pandas as pd\n", "import matplotlib.pyplot as plt\n", "import seaborn as sns\n", "%matpl...
0001/166/1166228.ipynb
s3://data-agents/kaggle-outputs/sharded/017_00001.jsonl.gz
{ "cells": [ { "cell_type": "markdown", "metadata": { "_cell_guid": "4c918118-cfd9-04e3-17b8-92e1787bf278" }, "source": [ "As shown in movie or novel, serial killers were always seeking random stranger to kill for their weird purpose, such as the zodiac, Jack the Ripper and so on. Therefore, thi...
0001/166/1166254.ipynb
s3://data-agents/kaggle-outputs/sharded/017_00001.jsonl.gz
{ "cells": [ { "cell_type": "markdown", "metadata": { "_cell_guid": "4c918118-cfd9-04e3-17b8-92e1787bf278" }, "source": [ "As shown in movie or novel, serial killers were always seeking random stranger to kill for their weird purpose, such as the zodiac, Jack the Ripper and so on. Therefore, thi...
0001/166/1166257.ipynb
s3://data-agents/kaggle-outputs/sharded/017_00001.jsonl.gz
{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "_cell_guid": "9e2764c6-55a2-5266-dac3-2d256920f860" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "macro.csv\n", "sample_submission.csv\n", "test.csv\n", "tr...
0001/166/1166265.ipynb
s3://data-agents/kaggle-outputs/sharded/017_00001.jsonl.gz
{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "_cell_guid": "7f64a313-52cd-c0a5-768f-88273e15d345" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "genderclassmodel.csv\n", "gendermodel.csv\n", "gendermodel.py\n"...
0001/166/1166302.ipynb
s3://data-agents/kaggle-outputs/sharded/017_00001.jsonl.gz
{ "cells": [ { "cell_type": "markdown", "metadata": { "_cell_guid": "a366bffb-96aa-2377-ac58-ab1ceabea0b5" }, "source": [ "# Exploratory Data Analysis" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "_cell_guid": "1212742a-7365-a1dc-9f35-ea772943d1c6" }, ...
0001/166/1166357.ipynb
s3://data-agents/kaggle-outputs/sharded/017_00001.jsonl.gz
{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "_cell_guid": "4e93e295-73cd-a757-b421-5d5734a8ed2b" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Iris.csv\n", "database.sqlite\n", "\n" ] } ], "so...
0001/166/1166359.ipynb
s3://data-agents/kaggle-outputs/sharded/017_00001.jsonl.gz
{ "cells": [ { "cell_type": "markdown", "metadata": { "_cell_guid": "b10b832c-c88b-1f81-14ce-7713ebab35a7" }, "source": [ "I am moving my first steps into machine learning and data science.\n", "This is a notebook where I will do an analysis on the Ames housing dataset. " ] }, { "c...
0001/166/1166365.ipynb
s3://data-agents/kaggle-outputs/sharded/017_00001.jsonl.gz
{ "cells": [ { "cell_type": "markdown", "metadata": { "_cell_guid": "e9ad4201-b787-1e76-9da0-f0ca23e153f0" }, "source": [] }, { "cell_type": "code", "execution_count": 1, "metadata": { "_cell_guid": "1ccfec8a-5773-d0d4-631e-de536377ba48" }, "outputs": [], "source": [ "im...
0001/166/1166377.ipynb
s3://data-agents/kaggle-outputs/sharded/017_00001.jsonl.gz
{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "_cell_guid": "4e93e295-73cd-a757-b421-5d5734a8ed2b" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Iris.csv\n", "database.sqlite\n", "\n" ] } ], "so...
0001/166/1166382.ipynb
s3://data-agents/kaggle-outputs/sharded/017_00001.jsonl.gz
{ "cells": [ { "cell_type": "markdown", "metadata": { "_cell_guid": "1c0645b0-256a-5833-cfdd-67e401be7d78" }, "source": [ "**Table of content**\n", "\n", "1. Real estate price per square meter vs GDP\n", "2. Real estate price per square meter vs micex\n", "3. Real estate price per...
0001/166/1166390.ipynb
s3://data-agents/kaggle-outputs/sharded/017_00001.jsonl.gz
{ "cells": [ { "cell_type": "markdown", "metadata": { "_cell_guid": "5d09b665-fabd-8ffb-dad2-10a6dcdfbd61" }, "source": [ "I've been playing around with this dataset but it seems to me that the charlist.csv provided does not seem to match up with the actual labels present in the dataset.csv file....
0001/166/1166416.ipynb
s3://data-agents/kaggle-outputs/sharded/017_00001.jsonl.gz
{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "_cell_guid": "f7fa5396-4ad0-71aa-68d1-7f5608cb7a7d" }, "outputs": [], "source": [ "# This Python 3 environment comes with many helpful analytics libraries installed\n", "# It is defined by the kaggle/python docker ...
0001/166/1166438.ipynb
s3://data-agents/kaggle-outputs/sharded/017_00001.jsonl.gz
{ "cells": [ { "cell_type": "markdown", "metadata": { "_cell_guid": "4adbd1a5-36f4-5485-035a-a35c443c71b5" }, "source": [ "This notebook is the reproduction of [A Very Extensive Sberbank Exploratory Analysis](https://www.kaggle.com/captcalculator/a-very-extensive-sberbank-exploratory-analysis) no...
0001/166/1166456.ipynb
s3://data-agents/kaggle-outputs/sharded/017_00001.jsonl.gz
{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "_cell_guid": "25a60fae-1793-cf5c-ad9a-f9fe379d0b32" }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 1, "metadata": { "_cell_guid": "9f887de1-8ee3-43c9-45e9-c03e965b0380" }, ...
0001/166/1166467.ipynb
s3://data-agents/kaggle-outputs/sharded/017_00001.jsonl.gz
{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "_cell_guid": "25a60fae-1793-cf5c-ad9a-f9fe379d0b32" }, "outputs": [], "source": [ "import pandas as pd\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "import seaborn as sns\n", "%matpl...
0001/166/1166475.ipynb
s3://data-agents/kaggle-outputs/sharded/017_00001.jsonl.gz
{ "cells": [ { "cell_type": "markdown", "metadata": { "_cell_guid": "48b28665-2587-814e-4706-60e280586d03" }, "source": [ "Trying to find Data insights from Indian Cities" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "_cell_guid": "0180b139-caf2-7b52-608c-f0...
0001/166/1166477.ipynb
s3://data-agents/kaggle-outputs/sharded/017_00001.jsonl.gz
{ "cells": [ { "cell_type": "markdown", "metadata": { "_cell_guid": "f4d524a1-1aa7-2810-6ce4-903e185f1bad" }, "source": [ "Let's check, what interesting facts can we find here..." ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "_cell_guid": "bc55fb8f-086c-ad87...
0001/166/1166496.ipynb
s3://data-agents/kaggle-outputs/sharded/017_00001.jsonl.gz
{ "cells": [ { "cell_type": "markdown", "metadata": { "_cell_guid": "986d2f40-3c0c-e41f-bf0c-d21549f607e6" }, "source": [ "No introduction needed,pal." ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "_cell_guid": "82bdedd2-aaaf-fcae-a64a-e7cd8c2147d6" }, ...
0001/166/1166508.ipynb
s3://data-agents/kaggle-outputs/sharded/017_00001.jsonl.gz
{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "_cell_guid": "a24bedf4-ea40-4db2-7836-2b0a8385c1b5" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "rec_8bit_ph03_cropC_kmeans_scale510.tif\n", "\n" ] } ], ...
0001/166/1166540.ipynb
s3://data-agents/kaggle-outputs/sharded/017_00001.jsonl.gz
{ "cells": [ { "cell_type": "markdown", "metadata": { "_cell_guid": "ad2b492c-a300-dc9f-30f7-599ce4cdca57" }, "source": [ "To explore and understand the Emergency service 911 call analysis" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "_cell_guid": "66a62df3...
0001/166/1166583.ipynb
s3://data-agents/kaggle-outputs/sharded/017_00001.jsonl.gz
{ "cells": [ { "cell_type": "markdown", "metadata": { "_cell_guid": "eac00dad-1680-659a-94c2-eba1c3a5ab27" }, "source": [ "# Initial EDA\n", "Take a look at the images and try out possible image processing techniques" ] }, { "cell_type": "markdown", "metadata": { "_cell_guid"...
0001/166/1166589.ipynb
s3://data-agents/kaggle-outputs/sharded/017_00001.jsonl.gz
{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "_cell_guid": "f7fa5396-4ad0-71aa-68d1-7f5608cb7a7d" }, "outputs": [], "source": [ "# This Python 3 environment comes with many helpful analytics libraries installed\n", "# It is defined by the kaggle/python docker ...
0001/166/1166605.ipynb
s3://data-agents/kaggle-outputs/sharded/017_00001.jsonl.gz
{ "cells": [ { "cell_type": "markdown", "metadata": { "_cell_guid": "7d8064b9-1369-9f84-c8cd-ddb7134140da" }, "source": [ "The National Hockey League is a professional ice hockey league with 30 teams based in\n", "Canada and the United States. The NHL is the greatest ice hockey league in the ...
0001/166/1166638.ipynb
s3://data-agents/kaggle-outputs/sharded/017_00001.jsonl.gz
{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "_cell_guid": "12813b60-b832-b436-2250-49c1f86954ed" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "genderclassmodel.csv\n", "gendermodel.csv\n", "gendermodel.py\n"...
0001/166/1166651.ipynb
s3://data-agents/kaggle-outputs/sharded/017_00001.jsonl.gz
{ "cells": [ { "cell_type": "markdown", "metadata": { "_cell_guid": "6f9936c7-8a9f-867f-c0cc-64071e5770c2" }, "source": [ "This is my first attempt on Kaggle. I am very closely following [this Python tutorial][1].\n", "\n", "\n", " [1]: https://www.kaggle.com/startupsci/titanic-data...
0001/166/1166672.ipynb
s3://data-agents/kaggle-outputs/sharded/017_00001.jsonl.gz
{ "cells": [ { "cell_type": "markdown", "metadata": { "_cell_guid": "4f3d15f5-9de3-bb94-3fef-b02ee6a227bd" }, "source": [ "Attempt at data exploration for Women In Kaggle" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "_cell_guid": "6f832330-7b07-a3ee-89c3-eb...
0001/166/1166711.ipynb
s3://data-agents/kaggle-outputs/sharded/017_00001.jsonl.gz
{ "cells": [ { "cell_type": "markdown", "metadata": { "_cell_guid": "b934473c-1fe1-7a54-3128-535411714f31" }, "source": [ "This notebook explores the .tif files based on the notebook: https://www.kaggle.com/fppkaggle/making-tifs-look-normal-using-spectral-fork/notebook" ] }, { "cell_typ...
0001/166/1166719.ipynb
s3://data-agents/kaggle-outputs/sharded/017_00001.jsonl.gz
{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "_cell_guid": "4c32c5a7-a425-94be-ba47-ef81320af38b", "collapsed": true }, "outputs": [], "source": [ "import pandas as pd\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "import seaborn...
0001/166/1166726.ipynb
s3://data-agents/kaggle-outputs/sharded/017_00001.jsonl.gz
{ "cells": [ { "cell_type": "markdown", "metadata": { "_cell_guid": "a366bffb-96aa-2377-ac58-ab1ceabea0b5" }, "source": [ "# Exploratory Data Analysis" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "_cell_guid": "1212742a-7365-a1dc-9f35-ea772943d1c6" }, ...
0001/166/1166729.ipynb
s3://data-agents/kaggle-outputs/sharded/017_00001.jsonl.gz
{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "_cell_guid": "ebdde620-547a-9a5c-e267-11f4353421e9" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "aisles.csv\n", "departments.csv\n", "order_products__prior.csv\n...
0001/166/1166791.ipynb
s3://data-agents/kaggle-outputs/sharded/017_00001.jsonl.gz
{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "_cell_guid": "63a53955-0076-2de8-a901-503d2e395067" }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=...
0001/166/1166834.ipynb
s3://data-agents/kaggle-outputs/sharded/017_00001.jsonl.gz
{ "cells": [ { "cell_type": "markdown", "metadata": { "_cell_guid": "e04aee16-ff10-5ebe-c57a-92d081062ce2" }, "source": [ "Do the number of items in an order grow as time goes on?" ] }, { "cell_type": "code", "execution_count": 15, "metadata"...
0001/166/1166843.ipynb
s3://data-agents/kaggle-outputs/sharded/017_00001.jsonl.gz
{ "cells": [ { "cell_type": "markdown", "metadata": { "_cell_guid": "e04aee16-ff10-5ebe-c57a-92d081062ce2" }, "source": [ "Do the number of items in an order grow as time goes on?" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "_cell_guid": "75726955-1586-472...
0001/166/1166848.ipynb
s3://data-agents/kaggle-outputs/sharded/017_00001.jsonl.gz
{ "cells": [ { "cell_type": "markdown", "metadata": { "_cell_guid": "e04aee16-ff10-5ebe-c57a-92d081062ce2" }, "source": [ "Do the number of items in an order grow as time goes on?" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "_cell_guid": "75726955-1586-472...
0001/166/1166857.ipynb
s3://data-agents/kaggle-outputs/sharded/017_00001.jsonl.gz
{ "cells": [ { "cell_type": "markdown", "metadata": { "_cell_guid": "b934473c-1fe1-7a54-3128-535411714f31" }, "source": [ "This notebook explores the .tif files based on the notebook: https://www.kaggle.com/fppkaggle/making-tifs-look-normal-using-spectral-fork/notebook" ] }, { "cell_typ...
0001/166/1166868.ipynb
s3://data-agents/kaggle-outputs/sharded/017_00001.jsonl.gz
{ "cells": [ { "cell_type": "markdown", "metadata": { "_cell_guid": "eae84bd9-7984-9051-9095-21c202618919" }, "source": [ "Initial exploration of the data from Instacart Market Basket. " ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "_cell_guid": "05f1c97a-cf...
0001/166/1166891.ipynb
s3://data-agents/kaggle-outputs/sharded/017_00001.jsonl.gz
{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "_cell_guid": "ef304279-fa5c-6fd0-798d-a26ea053755d" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "genderclassmodel.csv\n", "gendermodel.csv\n", "gendermodel.py\n"...
0001/166/1166933.ipynb
s3://data-agents/kaggle-outputs/sharded/017_00001.jsonl.gz
{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "_cell_guid": "cc6aa7e1-7e45-13a1-4c05-442e168c817e" }, "outputs": [ { "data": { "text/html": [ "<script>requirejs.config({paths: { 'plotly': ['https://cdn.plot.ly/plotly-latest.min']},});if(!window.Plotl...
0001/166/1166950.ipynb
s3://data-agents/kaggle-outputs/sharded/017_00001.jsonl.gz
{ "cells": [ { "cell_type": "markdown", "metadata": { "_cell_guid": "19e99671-c8f6-b744-d022-16684406628f" }, "source": [ "This kernel attempts to implement a probabilistic version of [Jiwon's \"Small Improvements\"][1]. Jiwon's idea is that predicted prices for the test set should be selected f...
0001/166/1166959.ipynb
s3://data-agents/kaggle-outputs/sharded/017_00001.jsonl.gz
{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "_cell_guid": "ac56f0d4-63ce-add5-85f9-10dd946088b2" }, "outputs": [], "source": [ "import numpy as np # linear algebra\n", "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n", "import mat...
0001/166/1166989.ipynb
s3://data-agents/kaggle-outputs/sharded/017_00001.jsonl.gz
{ "cells": [ { "cell_type": "markdown", "metadata": { "_cell_guid": "fbcdf1f6-536c-8db6-5856-f4258f993063" }, "source": [ "Most Popular movie type\n", "=====================================" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "_cell_guid": "8dc...
0001/167/1167038.ipynb
s3://data-agents/kaggle-outputs/sharded/017_00001.jsonl.gz
{ "cells": [ { "cell_type": "markdown", "metadata": { "_cell_guid": "f8057069-2b1f-4413-66a8-9e02a399e3aa" }, "source": [ "this is just a test" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "_cell_guid": "0877759d-3d59-96d3-9c4f-fad764a51a88" }, "output...
0001/167/1167056.ipynb
s3://data-agents/kaggle-outputs/sharded/017_00001.jsonl.gz
{ "cells": [ { "cell_type": "markdown", "metadata": { "_cell_guid": "698cb028-8f24-f5f8-cfc4-e9f45cee16c8" }, "source": "" }, { "cell_type": "code", "execution_count": 17, "metadata": { "_cell_guid": "fb90d844-1539-78c4-94f5-759709e086fb" }, ...
0001/167/1167084.ipynb
s3://data-agents/kaggle-outputs/sharded/017_00001.jsonl.gz
{ "cells": [ { "cell_type": "markdown", "metadata": { "_cell_guid": "eae84bd9-7984-9051-9095-21c202618919" }, "source": [ "Initial exploration of the data from Instacart Market Basket. " ] }, { "cell_type": "code", "execution_count": 6, "meta...
0001/167/1167219.ipynb
s3://data-agents/kaggle-outputs/sharded/017_00001.jsonl.gz
{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "_cell_guid": "5e781f32-49a0-ad6a-dd69-57fff59ad78a" }, "outputs": [], "source": [ "# This Python 3 environment comes with many helpful analytics libraries installed\n", "# It is defined by the kaggle/python docker ...
0001/167/1167235.ipynb
s3://data-agents/kaggle-outputs/sharded/017_00001.jsonl.gz
{ "cells": [ { "cell_type": "markdown", "metadata": { "_cell_guid": "baab4c3e-35dc-cc71-9ede-18e0a838783e" }, "source": [ "Cat v" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "_cell_guid": "b39ef270-04b0-75ae-fd77-5132f6dda79a" }, "outputs": [ { ...
0001/167/1167248.ipynb
s3://data-agents/kaggle-outputs/sharded/017_00001.jsonl.gz
{ "cells": [ { "cell_type": "markdown", "metadata": { "_cell_guid": "33569ed4-1595-350d-db2e-c0d4b9875d2b" }, "source": [ "探索各变量的Pearson Correlation。" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "_cell_guid": "cc6aa7e1-7e45-13a1-4c05-442e168c817e" }, ...
0001/167/1167267.ipynb
s3://data-agents/kaggle-outputs/sharded/017_00001.jsonl.gz
{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "_cell_guid": "ef304279-fa5c-6fd0-798d-a26ea053755d" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "genderclassmodel.csv\n", "gendermodel.csv\n", "gendermodel.py\n"...
0001/167/1167316.ipynb
s3://data-agents/kaggle-outputs/sharded/017_00001.jsonl.gz
{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "_cell_guid": "27df649e-3206-1794-28d8-0d761d232ead" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": "aisles.csv\ndepartments.csv\norder_products...
0001/167/1167448.ipynb
s3://data-agents/kaggle-outputs/sharded/017_00001.jsonl.gz
{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "_cell_guid": "14902a46-a978-05c1-fdc6-7991e3388a94" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "macro.csv\n", "sample_submission.csv\n", "test.csv\n", "tr...
0001/167/1167810.ipynb
s3://data-agents/kaggle-outputs/sharded/017_00001.jsonl.gz
{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "_cell_guid": "45c221a5-3cff-1dc3-dfbe-612fe28cf584" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "aisles.csv\n", "departments.csv\n", "order_products__prior.csv\n...
0001/167/1167822.ipynb
s3://data-agents/kaggle-outputs/sharded/017_00001.jsonl.gz
{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "_cell_guid": "27df649e-3206-1794-28d8-0d761d232ead" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": "aisles.csv\ndepartments.csv\norder_products...
0001/167/1167879.ipynb
s3://data-agents/kaggle-outputs/sharded/017_00001.jsonl.gz
{ "cells": [ { "cell_type": "markdown", "metadata": { "_cell_guid": "149d93a0-033e-e2db-3437-771020ada5f1" }, "source": "" }, { "cell_type": "code", "execution_count": 1, "metadata": { "_cell_guid": "5cc9e2a1-2f09-1a2f-87f9-88f78541caa1" }, ...
0001/167/1167890.ipynb
s3://data-agents/kaggle-outputs/sharded/017_00001.jsonl.gz
{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "_cell_guid": "33c47c34-809e-6137-99c4-43c6bf09442b" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Chicago_Crimes_2001_to_2004.csv\n", "Chicago_Crimes_2005_to_2007.csv\n...
0001/167/1167892.ipynb
s3://data-agents/kaggle-outputs/sharded/017_00001.jsonl.gz
{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "_cell_guid": "0d756b9f-2054-a030-de51-7e9553a50727" }, "outputs": [], "source": [ "import pandas as pd\n", "import numpy as np\n", "from pandas import Series,DataFrame" ] }, { "cell_type": "code", ...
0001/167/1167903.ipynb
s3://data-agents/kaggle-outputs/sharded/017_00001.jsonl.gz
{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "_cell_guid": "14e41831-f36c-2690-17a7-0bcc069c4f6e" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": "(42000, 784) (42000, 1)\n" } ...
0001/168/1168012.ipynb
s3://data-agents/kaggle-outputs/sharded/017_00001.jsonl.gz
{ "cells": [ { "cell_type": "markdown", "metadata": { "_cell_guid": "37d55dc9-89c9-7da8-2808-ef872b355c9c" }, "source": [ "#Correlation Heat Map and Scatter Matrix on some of basic features" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "_cell_guid": "2ecb40d...
0001/168/1168048.ipynb
s3://data-agents/kaggle-outputs/sharded/017_00001.jsonl.gz
{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "_cell_guid": "66e5e507-4d33-5891-c39a-20389657b894" }, "outputs": [], "source": [ "from pandas import read_csv\n", "data = read_csv(\"../input/kc_house_data.csv\")" ] }, { "cell_type": "code", "executi...
0001/168/1168087.ipynb
s3://data-agents/kaggle-outputs/sharded/017_00001.jsonl.gz
{ "cells": [ { "cell_type": "markdown", "metadata": { "_cell_guid": "5aca5a8c-9ca2-03c7-b4ba-51efeee6dec4" }, "source": [ "#Working \n", "\n", "\n", "----------" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "_cell_guid": "800884a7-9434-1830-72e1-...
0001/168/1168092.ipynb
s3://data-agents/kaggle-outputs/sharded/017_00001.jsonl.gz
{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "_cell_guid": "e0c1ab21-a926-2a48-1294-62221fd75a9a" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "basic_income_dataset_dalia.csv\n", "codebook_basicIncome.pdf\n", ...
0001/168/1168151.ipynb
s3://data-agents/kaggle-outputs/sharded/017_00001.jsonl.gz
{ "cells": [ { "cell_type": "markdown", "metadata": { "_cell_guid": "aa17786e-6f58-0070-5a56-f390fa663ae5" }, "source": [ "Titanic dataset" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "_cell_guid": "bb60a820-44d9-652a-4387-e131808ce221" }, "outputs": ...
0001/168/1168162.ipynb
s3://data-agents/kaggle-outputs/sharded/017_00001.jsonl.gz
{ "cells": [ { "cell_type": "markdown", "metadata": { "_cell_guid": "2391331d-48f7-ed3e-8a1f-4814c307fafe" }, "source": [ "Titanic : Machine Learning from Disaster" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "_cell_guid": "d629b0f2-b0b1-2dfb-4917-6fc9ed6bd...
0001/168/1168279.ipynb
s3://data-agents/kaggle-outputs/sharded/017_00001.jsonl.gz
{ "cells": [ { "cell_type": "markdown", "metadata": { "_cell_guid": "583f83c8-e53e-e36a-7c4f-323e31da5a2f" }, "source": [ "Introduction - yes" ] }, { "cell_type": "markdown", "metadata": { "_cell_guid": "2d8d7319-1569-5ef9-fb5a-bf648a1d11af" }, "source": [ "# We read da...
0001/168/1168297.ipynb
s3://data-agents/kaggle-outputs/sharded/017_00001.jsonl.gz