[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"sanity-ZG3z5Hw_kTwZ4AqTiZSwFjTmCrDZoZappZzdnyKUWFk":3,"sanity-ZsMXLWoZxZ8cUwzr8We6cftfTvP4HxK7K-Ivx12yOY0":232},{"data":4,"sourceMap":-1},{"latestPodcast":5,"latestReleases":14,"post":39,"recent":207},[6],{"_id":7,"publishedAt":8,"slug":9,"sponsored":12,"title":13},"d81860b7-7b72-4ba5-8ad5-3b77fd9a8e9b","2026-07-14T07:40:00.000Z",{"_type":10,"current":11},"slug","your-ai-is-only-as-responsible-as-you-are",null,"Your AI is only as responsible as you are",[15,21,27,33],{"_id":16,"publishedAt":17,"slug":18,"title":20},"eb5b66eb-9410-4329-83bb-22bbff39402a","2026-04-28T13:00:00.000Z",{"_type":10,"current":19},"turn-scattered-knowledge-into-trusted-intelligence","Turning scattered knowledge into trusted intelligence: Stack Internal 2026.3",{"_id":22,"publishedAt":23,"slug":24,"title":26},"369c2401-b62e-4a37-8ff8-bf603023ecad","2026-03-02T15:03:00.988Z",{"_type":10,"current":25},"what-s-new-at-stack-overflow-march-2026","What’s new at Stack Overflow: March 2026",{"_id":28,"publishedAt":29,"slug":30,"title":32},"5e9053a4-07ea-447c-91ea-29e0b6228537","2026-02-02T15:00:00.000Z",{"_type":10,"current":31},"what-s-new-at-stack-overflow-february-2026","What’s new at Stack Overflow: February 2026",{"_id":34,"publishedAt":35,"slug":36,"title":38},"a1b538eb-a8a6-46d0-80a1-ac70ec9bb935","2026-01-05T10:00:00.000-05:00",{"_type":10,"current":37},"what-s-new-at-stack-overflow-january-2026","What’s new at Stack Overflow: January 2026",{"_createdAt":40,"_id":41,"_rev":42,"_type":43,"_updatedAt":44,"author":45,"body":62,"comments":171,"dateUrl":172,"excerpt":173,"image":174,"legacyBody":177,"product":12,"publishedAt":180,"slug":181,"sponsored":12,"tags":183,"title":206,"visible":171},"2023-05-25T09:39:18Z","wp-post-18350","dgl3SCUzppW3U2LvCoSupg","blogPost","2023-07-13T14:56:01Z",[46],{"_createdAt":47,"_id":48,"_rev":49,"_type":50,"_updatedAt":51,"avatar":52,"bio":57,"employee":58,"name":59,"slug":60},"2023-05-23T16:27:18Z","wp-author-cap-17508","07ZbrKPSUrjrV4wQ6fDpaa","blogAuthor","2023-06-20T15:05:10Z",{"_type":53,"asset":54},"image",{"_ref":55,"_type":56},"image-8c28cb2ef9d5c7ef909c7685c3808e5c66f17aeb-400x400-jpg","reference","Curriculum Developer, Codecademy","none","Sophie Sommer",{"current":61},"sophie-sommer",[63,74,78,86,100,111,152],{"_key":64,"_type":65,"children":66,"markDefs":72,"style":73},"b451b3a34ce0","block",[67],{"_key":68,"_type":69,"marks":70,"text":71},"b451b3a34ce00","span",[],"In the sixth lesson of the series we'll discuss some methods for data transformation to improve a linear regression model. In the process, we'll learn to simulate data with known properties, review some of the assumptions of linear regression, and continue to practice our Python skills.",[],"normal",{"_key":75,"_type":76,"markDefs":12,"url":77},"77336906ec51","embed","https:\u002F\u002Fwww.youtube.com\u002Fembed\u002FSXAVxGhcYXw?start=106",{"_key":79,"_type":65,"children":80,"markDefs":85,"style":73},"1a3bc01d659d",[81],{"_key":82,"_type":69,"marks":83,"text":84},"1a3bc01d659d0",[],"Here are some Stack Overflow questions related to the work we did in today's session:",[],{"_key":87,"_type":65,"children":88,"level":94,"listItem":95,"markDefs":96,"style":73},"cdc4eb3d8abc",[89],{"_key":90,"_type":69,"marks":91,"text":93},"cdc4eb3d8abc0",[92],"12b1d3477603","More efficient way to mean center a subset of columns in a pandas dataframe and retain column names",1,"bullet",[97],{"_key":92,"_type":98,"href":99,"reference":12},"link","https:\u002F\u002Fstackoverflow.com\u002Fquestions\u002F34953988\u002Fmore-efficient-way-to-mean-center-a-sub-set-of-columns-in-a-pandas-dataframe-and",{"_key":101,"_type":65,"children":102,"level":94,"listItem":95,"markDefs":108,"style":73},"1febf7b893ce",[103],{"_key":104,"_type":69,"marks":105,"text":107},"1febf7b893ce0",[106],"d9e475168004","How to interpret results of Linear Regression after log-transforming the target variable?",[109],{"_key":106,"_type":98,"href":110,"reference":12},"https:\u002F\u002Fstackoverflow.com\u002Fquestions\u002F66623546\u002Fhow-to-interpret-results-of-linear-regression-after-log-transforming-the-target",{"_key":112,"_type":65,"children":113,"markDefs":145,"style":73},"be569eebce2f",[114,118,123,127,132,136,141],{"_key":115,"_type":69,"marks":116,"text":117},"be569eebce2f0",[],"If you want to ask any questions or provide feedback on the lesson, you are welcome to leave a comment on the ",{"_key":119,"_type":69,"marks":120,"text":122},"be569eebce2f1",[121],"97476045413e","YouTube recording of this lesson",{"_key":124,"_type":69,"marks":125,"text":126},"be569eebce2f2",[],". If you’d like to watch a session live, follow the ",{"_key":128,"_type":69,"marks":129,"text":131},"be569eebce2f3",[130],"5c4fb54ed3fd","Codecademy YouTube channel",{"_key":133,"_type":69,"marks":134,"text":135},"be569eebce2f4",[],". We'll be live again on Tuesday, June 29 at 11am EDT to discuss methods for comparing regression models. You can join that session ",{"_key":137,"_type":69,"marks":138,"text":140},"be569eebce2f5",[139],"35bea9761c52","here",{"_key":142,"_type":69,"marks":143,"text":144},"be569eebce2f6",[],".",[146,148,150],{"_key":121,"_type":98,"href":147,"reference":12},"https:\u002F\u002Fyoutu.be\u002FSXAVxGhcYXw?t=106",{"_key":130,"_type":98,"href":149,"reference":12},"https:\u002F\u002Fwww.youtube.com\u002Fc\u002Fcodecademy\u002Ffeatured",{"_key":139,"_type":98,"href":151,"reference":12},"https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=n1E8_OqB0Fs",{"_key":153,"_type":65,"children":154,"markDefs":168,"style":73},"415ae9ae520f",[155,159,164],{"_key":156,"_type":69,"marks":157,"text":158},"415ae9ae520f0",[],"Finally, if you want even more linear regression content, you can sign up for the ",{"_key":160,"_type":69,"marks":161,"text":163},"415ae9ae520f1",[162],"539c5d2458b3","Linear Regression in Python interactive course",{"_key":165,"_type":69,"marks":166,"text":167},"415ae9ae520f2",[]," this series was based on. This course was developed by Sophie and has many more quizzes, projects, and helpful nuggets that we can’t fit into our streams!",[169],{"_key":162,"_type":98,"href":170,"reference":12},"https:\u002F\u002Fwww.codecademy.com\u002Flearn\u002Flinear-regression-mssp?utm_source=stack_overflow&utm_medium=partners&utm_content=cclive_regression_1",true,"2021\u002F06\u002F26","",{"_type":53,"asset":175},{"_ref":176,"_type":56},"image-f5b272e299c874f83358613fe0855ad7f7ea164c-2400x1240-png",{"code":178,"language":179},"\u003C!-- wp:paragraph -->\n\u003Cp>In the sixth lesson of the series we'll discuss some methods for data transformation to improve a linear regression model. In the process, we'll learn to simulate data with known properties, review some of the assumptions of linear regression, and continue to practice our Python skills.\u003C\u002Fp>\n\u003C!-- \u002Fwp:paragraph -->\n\n\u003C!-- wp:html -->\n\u003Ciframe width=\"560\" height=\"560\" src=\"https:\u002F\u002Fwww.youtube.com\u002Fembed\u002FSXAVxGhcYXw?start=106\" title=\"YouTube video player\" frameborder=\"0\" allow=\"accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture\" allowfullscreen>\u003C\u002Fiframe>\n\u003C!-- \u002Fwp:html -->\n\n\u003C!-- wp:paragraph -->\n\u003Cp>Here are some Stack Overflow questions related to the work we did in today's session:\u003C\u002Fp>\n\u003C!-- \u002Fwp:paragraph -->\n\n\u003C!-- wp:jetpack\u002Fmarkdown {\"source\":\"- [More efficient way to mean center a subset of columns in a pandas dataframe and retain column names](https:\u002F\u002Fstackoverflow.com\u002Fquestions\u002F34953988\u002Fmore-efficient-way-to-mean-center-a-sub-set-of-columns-in-a-pandas-dataframe-and)\"} -->\n\u003Cdiv class=\"wp-block-jetpack-markdown\">\u003Cul>\n\u003Cli>\u003Ca href=\"https:\u002F\u002Fstackoverflow.com\u002Fquestions\u002F34953988\u002Fmore-efficient-way-to-mean-center-a-sub-set-of-columns-in-a-pandas-dataframe-and\">More efficient way to mean center a subset of columns in a pandas dataframe and retain column names\u003C\u002Fa>\u003C\u002Fli>\n\u003C\u002Ful>\n\u003C\u002Fdiv>\n\u003C!-- \u002Fwp:jetpack\u002Fmarkdown -->\n\n\u003C!-- wp:jetpack\u002Fmarkdown {\"source\":\"- [How to interpret results of Linear Regression after log-transforming the target variable?](https:\u002F\u002Fstackoverflow.com\u002Fquestions\u002F66623546\u002Fhow-to-interpret-results-of-linear-regression-after-log-transforming-the-target)\"} -->\n\u003Cdiv class=\"wp-block-jetpack-markdown\">\u003Cul>\n\u003Cli>\u003Ca href=\"https:\u002F\u002Fstackoverflow.com\u002Fquestions\u002F66623546\u002Fhow-to-interpret-results-of-linear-regression-after-log-transforming-the-target\">How to interpret results of Linear Regression after log-transforming the target variable?\u003C\u002Fa>\u003C\u002Fli>\n\u003C\u002Ful>\n\u003C\u002Fdiv>\n\u003C!-- \u002Fwp:jetpack\u002Fmarkdown -->\n\n\u003C!-- wp:paragraph -->\n\u003Cp>If you want to ask any questions or provide feedback on the lesson, you are welcome to leave a comment on the \u003Ca href=\"https:\u002F\u002Fyoutu.be\u002FSXAVxGhcYXw?t=106\">YouTube recording of this lesson\u003C\u002Fa>. If you’d like to watch a session live, follow the \u003Ca href=\"https:\u002F\u002Fwww.youtube.com\u002Fc\u002Fcodecademy\u002Ffeatured\">Codecademy YouTube channel\u003C\u002Fa>. We'll be live again on Tuesday, June 29 at 11am EDT to discuss methods for comparing regression models. You can join that session \u003Ca href=\"https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=n1E8_OqB0Fs\">here\u003C\u002Fa>.\u003C\u002Fp>\n\u003C!-- \u002Fwp:paragraph -->\n\n\u003C!-- wp:paragraph -->\n\u003Cp>Finally, if you want even more linear regression content, you can sign up for the \u003Ca href=\"https:\u002F\u002Fwww.codecademy.com\u002Flearn\u002Flinear-regression-mssp?utm_source=stack_overflow&utm_medium=partners&utm_content=cclive_regression_1\">Linear Regression in Python interactive course\u003C\u002Fa> this series was based on. This course was developed by Sophie and has many more quizzes, projects, and helpful nuggets that we can’t fit into our streams!\u003C\u002Fp>\n\u003C!-- \u002Fwp:paragraph -->","html","2021-06-26T12:56:00.000Z",{"current":182},"level-up-linear-regression-in-python-part-6",[184,192,197,202],{"_createdAt":185,"_id":186,"_rev":187,"_type":188,"_updatedAt":185,"slug":189,"title":191},"2023-05-23T16:43:21Z","wp-tagcat-code-for-a-living","9HpbCsT2tq0xwozQfkc4ih","blogTag",{"current":190},"code-for-a-living","Code for a Living",{"_createdAt":185,"_id":193,"_rev":187,"_type":188,"_updatedAt":185,"slug":194,"title":196},"wp-tagcat-level-up",{"current":195},"level-up","level up",{"_createdAt":185,"_id":198,"_rev":187,"_type":188,"_updatedAt":185,"slug":199,"title":201},"wp-tagcat-linear-regression",{"current":200},"linear-regression","linear regression",{"_createdAt":185,"_id":203,"_rev":187,"_type":188,"_updatedAt":185,"slug":204,"title":205},"wp-tagcat-python",{"current":205},"python","Level Up: Linear Regression in Python - Part 6",[208,214,220,226],{"_id":209,"publishedAt":210,"slug":211,"sponsored":12,"title":213},"76c9771b-34e6-4d98-8641-ecefc711f0ef","2026-07-06T15:23:34.559Z",{"_type":10,"current":212},"when-the-sensor-starts-thinking-snortml-agentic-ai-and-the-evolving-architecture-of-intrusion-detection","When the sensor starts thinking: SnortML, agentic AI, and the evolving architecture of intrusion detection",{"_id":215,"publishedAt":216,"slug":217,"sponsored":12,"title":219},"28e560af-f0aa-4d46-bd90-f435ad604aa7","2026-06-26T14:00:27.102Z",{"_type":10,"current":218},"paging-charity-how-can-engineering-leaders-avoid-becoming-bond-villains","Paging Charity! How can engineering leaders avoid becoming Bond villains?",{"_id":221,"publishedAt":222,"slug":223,"sponsored":12,"title":225},"4b22c2a3-3779-4966-93eb-5230391dbdce","2026-06-23T14:08:58.595Z",{"_type":10,"current":224},"your-ai-shipped-a-backend-that-boots-that-is-the-whole-problem","Your AI shipped a backend that boots. That is the whole problem.",{"_id":227,"publishedAt":228,"slug":229,"sponsored":12,"title":231},"5cf362e1-fe7b-45af-b69c-914731c6a052","2026-06-23T14:00:00.000Z",{"_type":10,"current":230},"the-2026-developer-survey-is-now-open-for-human-developers-only","The 2026 Developer Survey is now open (for human developers only)!",{"data":233,"sourceMap":-1},{"count":234,"lastTimestamp":12},0]