{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Introduction: Linear Regression with Medium Articles\n", "\n", "In this notebook, we'll look at performing some basic linear regression with the medium articles. This is a continuation of the data analysis performed on my Medium articles. " ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "ExecuteTime": { "end_time": "2018-12-27T03:24:38.358494Z", "start_time": "2018-12-27T03:24:36.875877Z" } }, "outputs": [ { "data": { "text/html": [ "" ], "text/vnd.plotly.v1+html": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Data science imports\n", "import pandas as pd\n", "import numpy as np\n", "\n", "from scipy import stats\n", "\n", "# Options for pandas\n", "pd.options.display.max_columns = 20\n", "\n", "# Display all cell outputs\n", "from IPython.core.interactiveshell import InteractiveShell\n", "InteractiveShell.ast_node_interactivity = 'all'\n", "\n", "# Interactive plotting\n", "import plotly.plotly as py\n", "import plotly.graph_objs as go\n", "from plotly.offline import iplot\n", "import cufflinks\n", "cufflinks.go_offline()\n", "\n", "%load_ext autoreload\n", "%autoreload 2\n", "\n", "from timeit import default_timer as timer\n", "\n", "from collections import Counter, defaultdict\n", "from itertools import chain\n", "\n", "from bs4 import BeautifulSoup\n", "import re\n", "\n", "import requests\n", "from multiprocessing import Pool" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "ExecuteTime": { "end_time": "2018-12-27T03:24:39.422536Z", "start_time": "2018-12-27T03:24:39.350187Z" } }, "outputs": [ { "data": { "text/html": [ "" ], "text/vnd.plotly.v1+html": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from utils import process_in_parallel, get_links, make_iplot" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "ExecuteTime": { "end_time": "2018-12-27T03:24:41.134418Z", "start_time": "2018-12-27T03:24:40.976549Z" } }, "outputs": [ { "data": { "text/plain": [ "\"Medium(function(i,s,o,g,r,a,m){i['GoogleAnalyticsObject']=r;i[r]=i[r]||function(){\\n(i[r].q=i[r].q||[\"" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "soup = BeautifulSoup(open('data/published.html', 'r'))\n", "soup.text[:100]" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "ExecuteTime": { "end_time": "2018-12-27T03:24:42.032639Z", "start_time": "2018-12-27T03:24:41.871801Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Found 212 entries.\n", "Total Read Time of Entries: 1361 minutes.\n" ] } ], "source": [ "links = get_links(soup)" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "ExecuteTime": { "end_time": "2018-12-27T03:24:54.163230Z", "start_time": "2018-12-27T03:24:44.240823Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Processed 212 entries in 10 seconds.\n" ] }, { "data": { "text/html": [ "
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clapsread_timetagstexttime_publishedtitleword_countresponseclaps_per_wordwords_per_minute<tag>Education<tag>Data Science<tag>Towards Data Science<tag>Machine Learning<tag>Python<tag>Programming<tag>Statistics<tag>Data Analysis<tag>Books<tag>Review
0661[]This wasn’t entirely serious, but I do think a...2018-11-28 19:44:26.105000-05:00response-2018-11-28 19:44:26.105000-05:00158response0.417722158.00000000000
101[]Thanks! I’m glad that others find my work usef...2018-11-28 20:01:40.532000-05:00response-2018-11-28 20:01:40.532000-05:0030response0.00000030.00000000000
301[]I wrote another article about deploying this t...2018-11-28 20:05:02.529000-05:00response-2018-11-28 20:05:02.529000-05:0078response0.00000078.00000000000
401[]These API’s are really fun (and sometimes usef...2018-12-01 20:30:50.845000-05:00response-2018-12-01 20:30:50.845000-05:0031response0.00000031.00000000000
501[]Thanks for your kind words! A lot of the time ...2018-12-15 13:56:37.536000-05:00response-2018-12-15 13:56:37.536000-05:0095response0.00000095.00000000000
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" ], "text/plain": [ " claps read_time tags text \\\n", "0 66 1 [] This wasn’t entirely serious, but I do think a... \n", "1 0 1 [] Thanks! I’m glad that others find my work usef... \n", "3 0 1 [] I wrote another article about deploying this t... \n", "4 0 1 [] These API’s are really fun (and sometimes usef... \n", "5 0 1 [] Thanks for your kind words! A lot of the time ... \n", "\n", " time_published title \\\n", "0 2018-11-28 19:44:26.105000-05:00 response-2018-11-28 19:44:26.105000-05:00 \n", "1 2018-11-28 20:01:40.532000-05:00 response-2018-11-28 20:01:40.532000-05:00 \n", "3 2018-11-28 20:05:02.529000-05:00 response-2018-11-28 20:05:02.529000-05:00 \n", "4 2018-12-01 20:30:50.845000-05:00 response-2018-12-01 20:30:50.845000-05:00 \n", "5 2018-12-15 13:56:37.536000-05:00 response-2018-12-15 13:56:37.536000-05:00 \n", "\n", " word_count response claps_per_word words_per_minute Education \\\n", "0 158 response 0.417722 158.0 0 \n", "1 30 response 0.000000 30.0 0 \n", "3 78 response 0.000000 78.0 0 \n", "4 31 response 0.000000 31.0 0 \n", "5 95 response 0.000000 95.0 0 \n", "\n", " Data Science Towards Data Science Machine Learning \\\n", "0 0 0 0 \n", "1 0 0 0 \n", "3 0 0 0 \n", "4 0 0 0 \n", "5 0 0 0 \n", "\n", " Python Programming Statistics Data Analysis \\\n", "0 0 0 0 0 \n", "1 0 0 0 0 \n", "3 0 0 0 0 \n", "4 0 0 0 0 \n", "5 0 0 0 0 \n", "\n", " Books Review \n", "0 0 0 \n", "1 0 0 \n", "3 0 0 \n", "4 0 0 \n", "5 0 0 " ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data = process_in_parallel(links)\n", "responses = data[data['response'] == 'response'].copy()\n", "articles = data[data['response'] == 'article'].copy()\n", "responses.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Linear Regression" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "ExecuteTime": { "end_time": "2018-12-27T03:24:55.791870Z", "start_time": "2018-12-27T03:24:55.757762Z" } }, "outputs": [ { "data": { "text/plain": [ "LinregressResult(slope=-0.1884772297103852, intercept=2383.6905181923216, rvalue=-0.12898903654458954, pvalue=0.17926807314380133, stderr=0.1394282852256092)" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "regression = stats.linregress(x=articles['word_count'], y=articles['claps'])\n", "slope = regression.slope\n", "intercept = regression.intercept\n", "rvalue = regression.rvalue\n", "\n", "regression" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "ExecuteTime": { "end_time": "2018-12-27T03:25:14.709811Z", "start_time": "2018-12-27T03:25:13.937964Z" } }, "outputs": [ { "data": { "text/html": [ "" ], "text/vnd.plotly.v1+html": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.plotly.v1+json": { "config": { "linkText": "Export to plot.ly", "plotlyServerURL": "https://plot.ly", "showLink": true }, "data": [ { "marker": { "color": "blue", "size": 12 }, "mode": "markers", "name": "observations", "text": [ "Please Steal My Articles", "Jupyter Notebook Extensions", "How to Write a Jupyter Notebook Extension", "Introduction to Interactive Time Series Visualizations with Plotly in Python", "Modeling: Teaching a Machine Learning Algorithm to Deliver Business Value", "Docker for Data Science Without the Hassle", "Estimating Probabilities with Bayesian Modeling in Python", "Python and Slack: A Natural Match", "Deploying a Python Web App on AWS", "Transfer Learning with Convolutional Neural Networks in PyTorch", "How to Create Value with Machine Learning", "Prediction Engineering: How to Set Up Your Machine Learning Problem", "Feature Engineering: What Powers Machine Learning", "The Power of I Don’t Know", "Recurrent Neural Networks by Example in Python", "Overcome Your Biases with Data", "Deploying a Keras Deep Learning Model as a Web Application in Python", "Featuretools on Spark", "Simpson’s Paradox: How to Prove Opposite Arguments with the Same Data", "Building a Recommendation System Using Neural Network Embeddings", "Converting Medium Posts to Markdown for Your Blog", "Five Minutes to Your Own Website", "Neural Network Embeddings Explained", "Practical Advice for Data Science Writing", "Another Machine Learning Walk-Through and a Challenge", "Wikipedia Data Science: Working with the World’s Largest Encyclopedia", "My Weaknesses as a Data Scientist", "How to Put Fully Interactive, Runnable Code in a Medium Post", "How to Visualize a Decision Tree from a Random Forest in Python using Scikit-Learn", "A “Data Science for Good” Machine Learning Project Walk-Through in Python: Part Two", "A “Data Science for Good“ Machine Learning Project Walk-Through in Python: Part One", "Parallelizing Feature Engineering with Dask", "An Implementation and Explanation of the Random Forest in Python", "The most important part of a data science project is writing a blog post", "How to get the right data? 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Y = 2683.88X + 678.66$", "x": 0, "y": 10050 } ], "height": 600, "title": "Claps vs Tag Towards Data Science", "width": 900, "xaxis": { "tickfont": { "size": 14 }, "title": "Towards Data Science", "titlefont": { "size": 16 } }, "yaxis": { "tickfont": { "size": 14 }, "title": "Claps", "titlefont": { "size": 16 } } } }, "text/html": [ "
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