Title: GitHub - NextAIML/Credit-Risk-Modeling-in-Python: Modeled the credit risk associated with consumer loans. Performed exploratory data analysis (EDA), preprocessing of continuous and discrete variables using various techniques depending on the feature. Checked for missing values and cleaned the data. Built the probability of default model using Logistic Regression. Visualized all the results. Computed Weight of Evidence and price elasticities. · GitHub
Open Graph Title: GitHub - NextAIML/Credit-Risk-Modeling-in-Python: Modeled the credit risk associated with consumer loans. Performed exploratory data analysis (EDA), preprocessing of continuous and discrete variables using various techniques depending on the feature. Checked for missing values and cleaned the data. Built the probability of default model using Logistic Regression. Visualized all the results. Computed Weight of Evidence and price elasticities.
X Title: GitHub - NextAIML/Credit-Risk-Modeling-in-Python: Modeled the credit risk associated with consumer loans. Performed exploratory data analysis (EDA), preprocessing of continuous and discrete variables using various techniques depending on the feature. Checked for missing values and cleaned the data. Built the probability of default model using Logistic Regression. Visualized all the results. Computed Weight of Evidence and price elasticities.
Description: Modeled the credit risk associated with consumer loans. Performed exploratory data analysis (EDA), preprocessing of continuous and discrete variables using various techniques depending on the feature. Checked for missing values and cleaned the data. Built the probability of default model using Logistic Regression. Visualized all the results. Computed Weight of Evidence and price elasticities. - GitHub - NextAIML/Credit-Risk-Modeling-in-Python: Modeled the credit risk associated with consumer loans. Performed exploratory data analysis (EDA), preprocessing of continuous and discrete variables using various techniques depending on the feature. Checked for missing values and cleaned the data. Built the probability of default model using Logistic Regression. Visualized all the results. Computed Weight of Evidence and price elasticities.
Open Graph Description: Modeled the credit risk associated with consumer loans. Performed exploratory data analysis (EDA), preprocessing of continuous and discrete variables using various techniques depending on the featu...
X Description: Modeled the credit risk associated with consumer loans. Performed exploratory data analysis (EDA), preprocessing of continuous and discrete variables using various techniques depending on the featu...
Opengraph URL: https://github.com/NextAIML/Credit-Risk-Modeling-in-Python
X: @github
Domain: github.com
| route-pattern | /:user_id/:repository |
| route-controller | files |
| route-action | disambiguate |
| fetch-nonce | v2:88f1a32a-2a72-3035-f0ba-fd689b3d08bd |
| current-catalog-service-hash | f3abb0cc802f3d7b95fc8762b94bdcb13bf39634c40c357301c4aa1d67a256fb |
| request-id | EA78:D8B6E:13AF84E:1A8965B:6A5961A2 |
| html-safe-nonce | 3a6edbc23010e6a2ac3efb30d971365334d13c78825d98931f68f164199eefd7 |
| visitor-payload | eyJyZWZlcnJlciI6IiIsInJlcXVlc3RfaWQiOiJFQTc4OkQ4QjZFOjEzQUY4NEU6MUE4OTY1Qjo2QTU5NjFBMiIsInZpc2l0b3JfaWQiOiI4MTQzNDY2NjQ0NTExNDgyMjc0IiwicmVnaW9uX2VkZ2UiOiJpYWQiLCJyZWdpb25fcmVuZGVyIjoiaWFkIn0= |
| visitor-hmac | da0aea4851a0f74ff588aa4d823aaf79577465468185fc0ec7188b09dfec78ec |
| hovercard-subject-tag | repository:291826880 |
| github-keyboard-shortcuts | repository,copilot |
| google-site-verification | Apib7-x98H0j5cPqHWwSMm6dNU4GmODRoqxLiDzdx9I |
| octolytics-url | https://collector.github.com/github/collect |
| analytics-location | / |
| fb:app_id | 1401488693436528 |
| apple-itunes-app | app-id=1477376905, app-argument=https://github.com/NextAIML/Credit-Risk-Modeling-in-Python |
| twitter:image | https://opengraph.githubassets.com/8b4f8b33e099b1f47a5306ce106bec3875a9d216b08dce0fd033aa5cea1eb960/NextAIML/Credit-Risk-Modeling-in-Python |
| twitter:card | summary_large_image |
| og:image | https://opengraph.githubassets.com/8b4f8b33e099b1f47a5306ce106bec3875a9d216b08dce0fd033aa5cea1eb960/NextAIML/Credit-Risk-Modeling-in-Python |
| og:image:alt | Modeled the credit risk associated with consumer loans. Performed exploratory data analysis (EDA), preprocessing of continuous and discrete variables using various techniques depending on the featu... |
| og:image:width | 1200 |
| og:image:height | 600 |
| og:site_name | GitHub |
| og:type | object |
| hostname | github.com |
| expected-hostname | github.com |
| None | a540949572872b935b393b36db38922db390ae71c859537d741b8f3eb7e545b5 |
| turbo-cache-control | no-cache |
| go-import | github.com/NextAIML/Credit-Risk-Modeling-in-Python git https://github.com/NextAIML/Credit-Risk-Modeling-in-Python.git |
| octolytics-dimension-user_id | 58768534 |
| octolytics-dimension-user_login | NextAIML |
| octolytics-dimension-repository_id | 291826880 |
| octolytics-dimension-repository_nwo | NextAIML/Credit-Risk-Modeling-in-Python |
| octolytics-dimension-repository_public | true |
| octolytics-dimension-repository_is_fork | true |
| octolytics-dimension-repository_parent_id | 251526119 |
| octolytics-dimension-repository_parent_nwo | vishnukanduri/Credit-Risk-Modeling-in-Python |
| octolytics-dimension-repository_network_root_id | 251526119 |
| octolytics-dimension-repository_network_root_nwo | vishnukanduri/Credit-Risk-Modeling-in-Python |
| turbo-body-classes | logged-out env-production page-responsive |
| disable-turbo | false |
| browser-stats-url | https://api.github.com/_private/browser/stats |
| browser-errors-url | https://api.github.com/_private/browser/errors |
| release | f4669eb12cd81bf213b8907a42667725d1583c79 |
| ui-target | full |
| theme-color | #1e2327 |
| color-scheme | light dark |
Links:
Viewport: width=device-width