| Factor | Type | Hypothesis |
|---|---|---|
| Age | Personal | A person above the age of 35 is more likely to stay than a younger person |
| Occupation | Professional | A person with a steady income is more likely to stay than a person who is in between jobs |
| Married | Relationship | A married person is more likely to stay than an unmarried person |
| Children | Relationship | People with children are more likely to stay than people without |
| Type of Service | Company | Location-based services could lead to higher churn rate if your customer base is young and unmarried |
| Monthly Charges | Company | High monthly charges may lead to termination of subscription |
| Period of Subscription | Company | Longer subscription period indicates loyal customers |
View nocors.json
| [ | |
| { | |
| "date": "2014-01-01", | |
| "value": 190000000 | |
| }, | |
| { | |
| "date": "2014-01-02", | |
| "value": 190379978 | |
| }, |
suriyadeepan
/ create_soil_type_feat.py
Created Nov 30, 2020
View create_soil_type_feat.py
| def create_feat_soil_type(df): | |
| soil_type_cols = [ col for col in df.columns if 'Soil_Type' in col ] | |
| soil_type = df[soil_type_cols].idxmax(axis=1) | |
| return soil_type.apply(lambda x : int(x[9:])) |
View amazon_src.json
| { | |
| src : ` | |
| <!doctype html><html lang="en-in" class="a-no-js" data-19ax5a9jf="dingo"><!-- sp:feature:head-start --> | |
| <head><script>var aPageStart = (new Date()).getTime();</script><meta charset="utf-8"/> | |
| <script type='text/javascript'>var ue_t0=ue_t0||+new Date();</script> | |
| <!-- sp:feature:cs-optimization --> | |
| <meta http-equiv='x-dns-prefetch-control' content='on'> | |
| <link rel="dns-prefetch" href="https://images-eu.ssl-images-amazon.com"> | |
| <link rel="dns-prefetch" href="https://m.media-amazon.com"> |
View model_factors.md
View model.md
| Factor | Type | Comments |
|---|---|---|
| Age | Personal | |
| Occupation | Professional | |
| Married | Relationship | |
| Children | Relationship | |
| Type of Service | Company | |
| Monthly Charges | Company | |
| Period of Subscription | Company |
View identify_target.md
| Question | Target Variable | Type |
|---|---|---|
| What sorts of people were more likely to survive? | Survival | Binary (Yes or No) |
| Predict (What is) the final price of each home. | Housing Price | Numerical (in USD) |
| Predict (What is) the probability that a driver will initiate an auto insurance claim in the next year. | Insurance Claim | Probability (Ranged [0-1] numerical) |
suriyadeepan
/ hyp_gen_data_science_pipeline.md
Last active Nov 16, 2020
Blog: Hypothesis Generation
View hyp_gen_data_science_pipeline.md
| Stage | Description | |
|---|---|---|
| 1 | Hypothesis Generation | Study the business problem. Build a conceptual model by developing a deeper understanding of the problem and domain. Generate Hypotheses. |
| 2 | Data Collection | Go out in the wild and collect data based on the generated hypotheses. |
| 3 | Study the variables | Identify potential predictors using data visualization |
| 4 | Data Preparation | Clean the data. Fill in missing data points. Scale, normalize and transform data as necessary. |
| 5 | Bivariate/Multivariate Analysis | Test the hypotheses you've generated earlier. Choose predictors based on correlation with target. |
| 6 | Data Transformation | Perform non-linear transformations (log) on variables to fish out non-l |
suriyadeepan
/ small_dom_blog.json
Created Oct 20, 2020
View small_dom_blog.json
| {"directed": false, "multigraph": false, "graph": {}, "nodes": [{"id": "html"}, {"id": "head"}, {"id": "meta"}, {"id": "meta_1"}, {"id": "meta_2"}, {"id": "title"}, {"id": "meta_3"}, {"id": "meta_4"}, {"id": "link"}, {"id": "link_1"}, {"id": "link_2"}, {"id": "link_3"}, {"id": "link_4"}, {"id": "link_5"}, {"id": "meta_5"}, {"id": "meta_6"}, {"id": "meta_7"}, {"id": "meta_8"}, {"id": "body"}, {"id": "nav"}, {"id": "div"}, {"id": "div_1"}, {"id": "button"}, {"id": "span"}, {"id": "span_1"}, {"id": "span_2"}, {"id": "span_3"}, {"id": "a"}, {"id": "div_2"}, {"id": "ul"}, {"id": "li"}, {"id": "a_1"}, {"id": "li_1"}, {"id": "a_2"}, {"id": "div_3"}, {"id": "a_3"}, {"id": "a_4"}, {"id": "a_5"}, {"id": "a_6"}, {"id": "a_7"}, {"id": "li_2"}, {"id": "a_8"}, {"id": "div_4"}, {"id": "div_5"}, {"id": "a_9"}, {"id": "img"}, {"id": "header"}, {"id": "div_6"}, {"id": "div_7"}, {"id": "div_8"}, {"id": "div_9"}, {"id": "div_10"}, {"id": "h1"}, {"id": "hr"}, {"id": "span_4"}, {"id": "div_11"}, {"id": "div_12"}, {"id": "div_13"}, |
View autobrowse.py
| from selenium import webdriver | |
| from selenium.webdriver.common.keys import Keys | |
| import time | |
| browser=webdriver.Chrome() | |
| #first tab | |
| browser.execute_script("window.open('about:blank', 'tab1');") | |
| browser.switch_to_window("tab1") |
View guide_trace_linear_regression.py
| {'b': {'args': (), | |
| 'fn': Normal(loc: 0.0, scale: 1.0), | |
| 'name': 'b', | |
| 'type': 'sample', | |
| 'value': tensor(-1.1897, grad_fn=<AddBackward0>)}, | |
| 'b_loc': {'args': (tensor(0.), Real()), | |
| 'fn': <function param.<locals>.fn at 0x127388950>, | |
| 'name': 'b_loc', | |
| 'type': 'param', | |
| 'value': tensor(0., requires_grad=True)}, |
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