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"## Regression Miniproject"
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"This miniproject is for groups of size 2.\n",
"\n",
"Find a dataset appropriate for regression analysis and, well, analyze it using regression:\n",
"- Identify the input variables and the output variable. For simplicity, ignore categorical variables in your data. Also if one of the variables that you care about has invalid values in a row (e.g., '?' or nan), discard that row. Your dataset should be such that after this step you have at least 4 input variables.\n",
"- Find the coefficients $\\theta$, assuming the sum of squares of errors as your cost function.\n",
"- Identify which 2 input variables give the smallest error.\n",
"- Is there anything that your analysis tells you that you did not know before?\n",
"\n",
"Some possible sources for data:\n",
"- [UCI Machine Learning Repository](http://archive.ics.uci.edu/ml/datasets.html?task=reg)\n",
"- [Scraping the web with beautifulsoup](https://www.crummy.com/software/BeautifulSoup/)\n",
"- [Kaggle](https://www.kaggle.com/datasets)\n",
"\n",
"**Your team must submit the following:**\n",
"- Submit any code you write, either in this notebook or as a seprate file.\n",
"- Summarize your work in one or two slides in this [Google Presenation](https://docs.google.com/presentation/d/1Mh_TxKfMGvK6HWPiNTlNUFuk2x7uvaqisHt3W_Hw9LM/edit?usp=sharing). All groups will add their work to the same presenation and so everyone can see work done by others. \n",
"\n",
"**Grading:**\n",
"- This miniproject has weight equal to half of a regular lab.\n",
"- 80% of the grade will be based on my evaluation of your work.\n",
"- 20% of the grade will be based on peer evaluation (more instructions will be provided later)."
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