{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"pygments_lexer":"ipython3","nbconvert_exporter":"python","version":"3.6.4","file_extension":".py","codemirror_mode":{"name":"ipython","version":3},"name":"python","mimetype":"text/x-python"}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"markdown","source":"<div \n     style=\"padding: 20px; \n            color: white;\n            font-size: 250%;\n            text-align: center;\n            display: fill;\n            border-radius: 5px;\n            background-color: #66023C;\n            overflow: hidden;\n            font-weight: 700;\n            border: 5px solid #F28C28;\"\n     >\n    🏆 Machine Learning Grandmaster\n</div>","metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19"}},{"cell_type":"markdown","source":"<div \n     style=\"padding: 20px; \n            color: #3b3b3b;\n            font-size: 115%;\n            text-align: left;\n            display: fill;\n            border-radius: 5px;\n            background-color: #e8f4ff;\n            overflow: hidden;\n            font-weight: 600;\n            border: 5px solid #ffcfa0;\"\n     >\n    Welcome to ML Grandmaster, where we'll take a look at the most important Machine Learning algorithms and fundamental concepts from the ground up. <br>\n    We'll look at the theory behind the concepts and also the code to implement these ideas in the real world.\n</div>","metadata":{}},{"cell_type":"markdown","source":"## ML Grandmaster Notebooks\n<br>\n<div\n     style=\"float: left; position: relative; width: 100%;\"\n     >\n    <table\n           style=\"float: left; font-size: 16px\"\n           >\n      <tr>\n        <th><b>📈 Machine Learning Fundamentals</b></th>\n        <th><b>🌳 Decision Trees</b></th>\n      </tr>\n      <tr>\n        <td><a href=\"https://www.kaggle.com/chazzer/ml-grandmaster-linear-regression/\">Linear Regression</a></td>\n        <td><a href=\"https://www.kaggle.com/code/chazzer/ml-grandmaster-decision-tree-classifier\">Decision Tree Classification</a></td>\n      </tr>\n      <tr>\n        <td>Logistic Regression [work in progress]</td>\n        <td><a href=\"https://www.kaggle.com/code/chazzer/ml-grandmaster-decision-tree-regressor\">Decision Tree Regression</a></td>\n      </tr>\n      <tr>\n        <td></td>\n        <td><a href=\"https://www.kaggle.com/code/chazzer/ml-grandmaster-random-forest/\">Random Forest</a></td>\n      </tr>\n      <tr>\n        <td></td>\n        <td><a href=\"https://www.kaggle.com/chazzer/ml-grandmaster-gradient-boosting-xgboost/\">Gradient Boosting (XGBoost)</a></td>\n      </tr>\n    </table>\n</div>","metadata":{}},{"cell_type":"markdown","source":"<div \n     style=\"padding: 20px; \n            color: white;\n            font-size: 250%;\n            text-align: center;\n            display: fill;\n            border-radius: 5px;\n            background-color: #023c66;\n            overflow: hidden;\n            font-weight: 700;\n            border: 5px solid #7ace07;\"\n     >\n    📈 Linear Regression\n</div>","metadata":{}},{"cell_type":"markdown","source":"<div \n     style=\"padding: 20px; \n            color: #3b3b3b;\n            font-size: 115%;\n            text-align: left;\n            display: fill;\n            border-radius: 5px;\n            background-color: #d7ffd7;\n            overflow: hidden;\n            font-weight: 600;\n            border: 5px solid #ffcfa0;\"\n     >\n    In this ML Grandmaster notebook, we'll be taking a look at Linear Regression from the ground up. <br>\n</div>","metadata":{}},{"cell_type":"markdown","source":"![2bbctk.jpg](attachment:d512d64d-f816-4a64-9a5c-8f9fa7efef94.jpg)","metadata":{},"attachments":{"d512d64d-f816-4a64-9a5c-8f9fa7efef94.jpg":{"image/jpeg":"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"}}},{"cell_type":"markdown","source":"# 👉 Index\n* [📚 Prerequisites and Notes](#0)\n* [🤨 Intuition](#1)\n* [🚚 Import](#2)\n    - [Packages](#2.1)\n    - [Dataset](#2.2)\n* [♻️ Linear Least Squares](#3)\n    - [Results](#3.1)\n    - [Proof](#3.2)\n    - [Note: x as a vector](#3.3)\n* [🧱 Fitting the Line](#4)\n    - [Fit](#4.1)\n    - [Evaluation: $ R^2 $](#4.2)\n* [💻 Code Implementation](#5)\n* [🙇 Conclusion](#6)","metadata":{}},{"cell_type":"markdown","source":"<a id=\"0\"></a>\n# 📚 Prerequisites and Notes","metadata":{}},{"cell_type":"markdown","source":"<div \n     style=\"padding: 20px; \n            color: #3b3b3b;\n            font-size: 115%;\n            text-align: left;\n            display: fill;\n            border-radius: 5px;\n            background-color: #d7ffd7;\n            overflow: hidden;\n            font-weight: 600;\n            border: 5px solid #ffcfa0;\"\n     >\n    There are no prerequisites for this notebook.\n</div>","metadata":{}},{"cell_type":"markdown","source":"<div \n     style=\"padding: 20px; \n            color: #3b3b3b;\n            font-size: 115%;\n            text-align: left;\n            display: fill;\n            border-radius: 5px;\n            background-color: #d7ffd7;\n            overflow: hidden;\n            font-weight: 600;\n            border: 5px solid #ffcfa0;\"\n     >\n    While Linear Regression is a simple model, it's very explainable and a great tool for starting off your Machine Learning journey.\n</div>","metadata":{}},{"cell_type":"markdown","source":"<a id=\"1\"></a>\n# 🤨 Intuition","metadata":{}},{"cell_type":"markdown","source":"<div \n     style=\"padding: 20px; \n            color: #3b3b3b;\n            font-size: 115%;\n            text-align: left;\n            display: fill;\n            border-radius: 5px;\n            background-color: #d7ffd7;\n            overflow: hidden;\n            font-weight: 600;\n            border: 5px solid #ffcfa0;\"\n     >\n    Given 2 variables some sample data $ \\{x_i, y_i\\}_{i=1}^{n} $ (which means $ n $ samples of 2 variables $ x $ and $ y $), we want to find the best line that <i>fits</i> this data. <br>\n    We will consider $ x_i $ as our input variable that will help us model $ y_i $, our output variable. <br>\n    We will choose an estimator $ \\hat{y}_i $, which depends on the input variable $ x_i $, in the form of a line function (this is why it's called <u>Linear</u> Regression).\n    $$ \\hat{y}_i = a + b x_i 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\n     style=\"padding: 20px; \n            color: #3b3b3b;\n            font-size: 115%;\n            text-align: left;\n            display: fill;\n            border-radius: 5px;\n            background-color: #d7ffd7;\n            overflow: hidden;\n            font-weight: 600;\n            border: 5px solid #ffcfa0;\"\n     >\n    The <u>Regression</u> part of Linear Regression revolves around finding the best line that fits the data, which means finding the best parameters $ a $ and $ b $ in our estimator function. <br>\n    To do this, we'll compute the <u>Sum of Squared Residuals</u> $ S(a, b) $ and minimize it to find the best parameters. <br>\n    Our Residuals will be the  difference between output and predicted output variables $ y_i - \\hat{y}_i $. <br>\n    We'll then go through the fitting/learning process on a simplified version of the House Prices dataset.\n</div>","metadata":{}},{"cell_type":"markdown","source":"<a id=\"2\"></a>\n# 🚚 Import","metadata":{}},{"cell_type":"markdown","source":"<a id=\"2.1\"></a>\n## Packages","metadata":{}},{"cell_type":"code","source":"import numpy as np  # linear algebra\nimport pandas as pd  # data manipulation\n\nfrom sklearn.linear_model import LinearRegression  # model\n\nfrom matplotlib import pyplot as plt  # visualization","metadata":{"execution":{"iopub.status.busy":"2022-10-16T16:32:00.148384Z","iopub.execute_input":"2022-10-16T16:32:00.148830Z","iopub.status.idle":"2022-10-16T16:32:01.459703Z","shell.execute_reply.started":"2022-10-16T16:32:00.148731Z","shell.execute_reply":"2022-10-16T16:32:01.458783Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"<a id=\"2.2\"></a>\n## Dataset","metadata":{}},{"cell_type":"code","source":"df = pd.read_csv('../input/house-prices-advanced-regression-techniques/train.csv')","metadata":{"execution":{"iopub.status.busy":"2022-10-16T16:32:01.461732Z","iopub.execute_input":"2022-10-16T16:32:01.463166Z","iopub.status.idle":"2022-10-16T16:32:01.513527Z","shell.execute_reply.started":"2022-10-16T16:32:01.463115Z","shell.execute_reply":"2022-10-16T16:32:01.512165Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"df = df[['GrLivArea', 'SalePrice']]","metadata":{"execution":{"iopub.status.busy":"2022-10-16T16:32:01.515345Z","iopub.execute_input":"2022-10-16T16:32:01.515723Z","iopub.status.idle":"2022-10-16T16:32:01.528937Z","shell.execute_reply.started":"2022-10-16T16:32:01.515690Z","shell.execute_reply":"2022-10-16T16:32:01.527778Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"df.plot.scatter(x='GrLivArea', y='SalePrice')","metadata":{"execution":{"iopub.status.busy":"2022-10-16T16:32:01.531621Z","iopub.execute_input":"2022-10-16T16:32:01.532202Z","iopub.status.idle":"2022-10-16T16:32:01.844910Z","shell.execute_reply.started":"2022-10-16T16:32:01.532166Z","shell.execute_reply":"2022-10-16T16:32:01.843450Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# let's get rid of some outliers\ndf = df.copy().loc[(df['GrLivArea'] < 4000) & (df['SalePrice'] < 600000)]","metadata":{"execution":{"iopub.status.busy":"2022-10-16T16:32:01.846659Z","iopub.execute_input":"2022-10-16T16:32:01.847160Z","iopub.status.idle":"2022-10-16T16:32:01.857276Z","shell.execute_reply.started":"2022-10-16T16:32:01.847111Z","shell.execute_reply":"2022-10-16T16:32:01.855987Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"df.plot.scatter(x='GrLivArea', y='SalePrice')","metadata":{"execution":{"iopub.status.busy":"2022-10-16T16:32:01.859058Z","iopub.execute_input":"2022-10-16T16:32:01.860509Z","iopub.status.idle":"2022-10-16T16:32:02.139447Z","shell.execute_reply.started":"2022-10-16T16:32:01.860460Z","shell.execute_reply":"2022-10-16T16:32:02.137488Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"<a id=\"3\"></a>\n# ♻️ Linear Least Squares","metadata":{}},{"cell_type":"markdown","source":"<a id=\"3.1\"></a>\n## Results","metadata":{}},{"cell_type":"markdown","source":"<div \n     style=\"padding: 20px; \n            color: #3b3b3b;\n            font-size: 115%;\n            text-align: left;\n            display: fill;\n            border-radius: 5px;\n            background-color: #d7ffd7;\n            overflow: hidden;\n            font-weight: 600;\n            border: 5px solid #ffcfa0;\"\n     >\n    To find the best line that fits our data, firstly we'll compute the Sum of Squared Residuals $ S(a, b) $ of our output variable $ y_i $ and our prediction $ \\hat{y}_i = a + b x_i $. <br>\n    <br>\n    $$ S(a, b) = \\sum_{i=1}^n (y_i - a - b x_i)^2 $$\n</div>","metadata":{}},{"cell_type":"markdown","source":"<div \n     style=\"padding: 20px; \n            color: #3b3b3b;\n            font-size: 115%;\n            text-align: left;\n            display: fill;\n            border-radius: 5px;\n            background-color: #d7ffd7;\n            overflow: hidden;\n            font-weight: 600;\n            border: 5px solid #ffcfa0;\"\n     >\n    The great thing about Linear Regression is that we can find the minimum of $ S(a, b) $ analytically, without approximations or Gradient Descent algorithms. <br>\n    In mathematics, we call this finding a <i>closed form</i> solution for $ a $ and $ b $. <br>\n    This is quite rare actually. <br>\n    The parameters of Logistic Regression, for example, do not have a closed form solution, so we have to rely on approximations.\n</div>","metadata":{}},{"cell_type":"markdown","source":"<div \n     style=\"padding: 20px; \n            color: #3b3b3b;\n            font-size: 115%;\n            text-align: left;\n            display: fill;\n            border-radius: 5px;\n            background-color: #d7ffd7;\n            overflow: hidden;\n            font-weight: 600;\n            border: 5px solid #ffcfa0;\"\n     >\n    By taking the derivatives of $ S(a, b) $ with respect to $ a $ and $ b $, setting them to 0, and solving the resulting system of equations, we find: \n    <br>\n    <br>\n    $$ b = \\frac{\\sum_{i=1}^n (y_i - \\overline{y}) (x_i - \\overline{x})}{\\sum_{i=1}^n (x_i - \\overline{x})^2} $$\n    <br>\n    $$ a = \\overline{y} - b \\overline{x} $$\n    <br>\n    With $ \\overline{y} = \\frac{1}{n}\\sum_{i=1}^n y_i $ and $ \\overline{x} = \\frac{1}{n}\\sum_{i=1}^n x_i $ being the variable means. <br>\n    Let's prove these results below.\n</div>","metadata":{}},{"cell_type":"markdown","source":"<a id=\"3.2\"></a>\n## Proof","metadata":{}},{"cell_type":"markdown","source":"<div \n     style=\"padding: 20px; \n            color: #3b3b3b;\n            font-size: 115%;\n            text-align: left;\n            display: fill;\n            border-radius: 5px;\n            background-color: #d7ffd7;\n            overflow: hidden;\n            font-weight: 600;\n            border: 5px solid #ffcfa0;\"\n     >\n    Let's start taking a look at how $ S(a, b) $ depends on both $ a $ and $ b $. <br>\n    $$ S(a, b) = \\sum_{i=1}^n (y_i - a - b x_i)^2 $$\n    $ S(a, b) $ is a positive quadratic function in both $ a $ and $ b $, which means that it will have a minimum for both $ a $ and $ b $.\n</div>","metadata":{}},{"cell_type":"markdown","source":"![2.jpg](attachment:b226c28e-6923-4854-8cbb-fec18b294bdb.jpg)","metadata":{},"attachments":{"b226c28e-6923-4854-8cbb-fec18b294bdb.jpg":{"image/jpeg":"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\n     style=\"padding: 20px; \n            color: #3b3b3b;\n            font-size: 115%;\n            text-align: left;\n            display: fill;\n            border-radius: 5px;\n            background-color: #d7ffd7;\n            overflow: hidden;\n            font-weight: 600;\n            border: 5px solid #ffcfa0;\"\n     >\n    So to find the values $ a_{min} $ and $ b_{min} $ that minimize the function $ S(a, b) $, all we'll have to do is take the derivatives, set them to 0, and solve the resulting system of equations. <br>\n    Let's do that and simplify the equations found via some algebra.\n</div>","metadata":{}},{"cell_type":"markdown","source":"<div \n     style=\"padding: 20px; \n            color: #3b3b3b;\n            font-size: 115%;\n            text-align: left;\n            display: fill;\n            border-radius: 5px;\n            background-color: #d7ffd7;\n            overflow: hidden;\n            font-weight: 600;\n            border: 5px solid #ffcfa0;\"\n     >\n    So to find the values $ a_{min} $ and $ b_{min} $ that minimize the function $ S(a, b) $, all we'll have to do is take the derivatives, set them to 0, and solve the resulting system of equations. <br>\n    Let's do that and simplify the equations found via some algebra.\n</div>","metadata":{}},{"cell_type":"markdown","source":"![3.jpg](attachment:c5ff9b68-8538-4c35-a3ec-37b5b946d887.jpg)","metadata":{},"attachments":{"c5ff9b68-8538-4c35-a3ec-37b5b946d887.jpg":{"image/jpeg":"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\n     style=\"padding: 20px; \n            color: #3b3b3b;\n            font-size: 115%;\n            text-align: left;\n            display: fill;\n            border-radius: 5px;\n            background-color: #d7ffd7;\n            overflow: hidden;\n            font-weight: 600;\n            border: 5px solid #ffcfa0;\"\n     >\n    Let's solve the first equation to find $ a 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\n     style=\"padding: 20px; \n            color: #3b3b3b;\n            font-size: 115%;\n            text-align: left;\n            display: fill;\n            border-radius: 5px;\n            background-color: #d7ffd7;\n            overflow: hidden;\n            font-weight: 600;\n            border: 5px solid #ffcfa0;\"\n     >\n    Now let's substitute our result for $ a $ in the second equation, solve it, and find $ b $.\n</div>","metadata":{}},{"cell_type":"markdown","source":"![5.jpg](attachment:53fd3f09-d494-4e6c-a37f-b5d5f99fcc09.jpg)","metadata":{},"attachments":{"53fd3f09-d494-4e6c-a37f-b5d5f99fcc09.jpg":{"image/jpeg":"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8QQSsO0CPJ+qjFebWP7Nbl86h4kGO4gtsn82P8ASuw0r4B+C7AKbqK71Bx1M85VT+CYoA6Lw78T/CXinUF0/S9UD3bZ2QyRNGXwMnGRzxXX1xkXwq8IWmq6fqVhpn2G5sZfNja2kK7z6N1yK7OgAooooAKKKrw31pcXEtvBdQSzQ8SxpIGZP94DkfjQBYooooAKKKKACuf8W6/N4fsIJ4Io5HklCYfOMYyeldBXC/E4/wDEqsh/03P/AKCaDqwVONSvGMlozt4n8yJHxjcoP50+oLM5sYD/ANM1/kKnoOZqzCiivCNc+OHimw1m6s7bwf5aQyMii4WVnIBIzwAOfagR7vRXzoPil8WNZfbpnhsxg9DFp0jY/FjipotL+OniA/v76XT427vLHDj8EG6gD6FzimCWNjgOpPoDXiNp8Ddf1BvM8ReN7yUnkpAzv/485/pXT6R8EfDWkX1rereatNcW0iyqXusAspyMgAcUAelUUUUAFeUfF/XPF+nX2jWHh+WWzsr5/KlvIYC7LIWwFyAcDHPAyea9XrJ8S+IbDwtoNzrGpOVt4B0XlnY8BQPUmgDxXxLp/j/4b2Fvr9x45fUW+0pF9ikDMspPbBPP6V75BI0tvHI6FGZQxU9iR0r5gT4jW/i3x3DrfiCzvLuGxbOl6LZx78vnhnJ6ngHocnHYV9HeHNVu9a0O3v73Sp9LnlzutZzl0wcDP1HNAGrRRRQAUUUUAFFFFABXC/EmxY2dnqcQ+e2k2sw6gHkfqP1rtbq5hs7WS5ncJFGpZmPYV55DDe/EDVmnnaS30a3bCoOC3/1/U9q78BFxqe3btGO/+Xqc2KacfZrVsut8RhIALHSri52KDK+cAcc9Af1rrNG1aHWtKhvoVKrIDlCclSOoqS2021srA2dpBHDFtK7QvXjv61wcHhXxdpMZi07UohEGLBEk2jJ9iKvlw1eLUPca2u9yb1qbTl7y8uh6PRXnR1zxto/N9p4uYl6t5ef1T/CtLTPiNpty4ivoZLOToWPzJ+J6j8qzll9ZLmhaS8ncuOKpt2lo/M7OimQzR3ESywyLJGwyrKcg0+uFqx0BRRRQAUUUUAFFFFABRRRQAUUVBeXttp1nLd3lxHb28S7pJZGCqo9STQBPXhXxM8Sz/EHxBbfD7wswmQzBr+6XlBtPTP8AdXqT3OAKj8S/EbXPiLqknhXwBDKts/y3OoHKEp0Jz/Anv1PavSfh/wDD7TfAWkeRb4nvpgDdXbLhpD6D0UdhQB0GhaPbaBoVlpNoD5FpCsSE9TjufcnJ/GtCiigAooooAp6vp0er6Ne6bKdsd1A8LNjOAykZ/WvmK40/VPgZ8RNPuPtDXdjNGPMdV2ieM8OuMnkHkfhX1TXzt8bfE6+LNetPBehWYvrq2mJeRF3N5uOY0+g+99PagDrta+LtxrGqp4f+H1j/AGrqMgBa7cYhhHr747k4H1r0rQotUg0O0j1q5hudSWP/AEiWFdqM3sK8S/Z112wiOpeH5raKHUi3npLtw8yDgof90849z6V79QAVV1HUbTSdOnv76dYLWBC8sj9FAq1XKfErQ7zxF8PtX02wybqSINGgON5Vg238cYoAn8GeNdO8cadc32mQ3McEM5hzOm3fgZyPbBrpK84+B5uo/hxb2l3p01lJbTyx/vUKGXnduwee+Pwr0egDA8cagdL8Da5eg4aKylKn3KkD9TXm/wCzjY+T4P1O9K4Nxe7AfUIg/qxrqvjPcG3+FOtEHBkWOP8AORaofAWIR/C20b/npczOf++sf0oA9Mrn/G/iCXwv4M1PWYYfNmtosxqRkbiQoJ9gTk10FZ2vaTDrugX+lT/6u7geEn0yOD+BwaAMb4c+KZPGPgmx1e4CLdNujnWMYUOpwcDPpg/jXVV8/wDwF1mbRPEOs+C9RzHN5jSRK3aRPlcD6gA/hXt3iDU10bw7qOpscC1tpJvxVSR+tAHn9v471nX/AIz/APCPaI8X9i6bG39oMUDb2HBw3UYYhR9DXqVeM/s8aa3/AAj2ra7ON1xf3hTeepCjJ/8AHmNezUAFFFcZ8SfFOr+FvD8cmh6TNf391L5ERSMusRI+8wHJ9AKAOzor50ii+O2uv5wku7RG5AdorcD/AID1r0L4fWfxNsNQMXiy5s7rTmVjvMgaZGwMAbQBg+9AHpNY3iTxNYeGNP8AtV6xLMdsUSfekPt/jWvJIsUbSOwVFBZiewFeQaTbv8SPHNxqF4GOk2RwkZ6Fc/Kv49TXbg8PGo5VKukI6v8ARfMaOs0D4m6NrVxHayrJZXMr7ESXlWPb5hx+ddrXz/fWIk+KsloNkCnUhj+EKuQR+lfQFbZlhqVFwlS2kr27A0FFFcR8VL3xPp/g1rrwr5n2yOZTL5UQkfyuc4Bz3xnjpXmCO3or508P/tCajpenJZ6/pE1/eIx3XAkETMM8Artxx0r0Pwb8Xbbxhq8Onw+H9Vtml3fv2QNEmBn5mHSgD0iuC+KH/Hhp4/6av/6DXe1xHxLtJZ9JtJ40LJDKd+BnAIwKDsy9pYmF/wCtC/eeK7DQNHsllJmuWt0KwoecbRyT2FYa/Ea+BE0mjYtc8sGb+eMUzwf4Wk1Cc6trEbMnHkxSj7+O5B7DsK6bxfqVnpvh64hl2F54zHFF6k98eg60HW4YeFVUVHnk93f8jV0zUrfVtPivLViY5B36g9wfcV8/+ONV8QfDL4xT+JFUz6fqYB2nOyWMABo89mXGR9R716/4Biay8JedcsI43keUFjgBPU+3BNeJ+MPEutfGXxUnhvw3b/8AEpt5Nyuy8Njgyu38K+g9+5NB52IhGnVlCOyZ1Xib9oK22Q2nhLT5L29mVf3k6HajEfdCDlmHT0z611Pw4T4jXFzLqPjG5gSzmh/dWflqsitnhjtHy8Z4JJrw+zGvfBLx0JdQ0yC6iIKrIyZWaP8AvRvjKt/+o19IeDvHeieOLBrjSZ282IDz7eUbZIiemR6cHkUGJ0pIVSzEADkk9q47wv8AEzQfF+vXek6Ut48lsrMZnhxEwBxw2e+eM4zXYsquhVgCrDBB7iuT8J/DvRfBep6ne6R9oQX5GYXfKRAZOFH1J65oA62iiigDK8TWceoeFtVs5VDJNaSoQfdTXmf7Od08vgW+t2OVhv22+2UU16lrcqwaDqMzkBY7aViT6BTXlP7OMLJ4K1KUg7ZL8498ItAHstFZfiS+m07w7f3Vtc2dtcpCxhlvH2xK+ONx9K8W8Pr8SPEoGvv8QNNttNjlKySRuDGhHVSm0L+Z75oA99qtqN/b6VptzqF3II7e2iaWRvRQMmodN1nTdV8xbDUrW8aHaJfIlV9uRxnB4zXB/He/lsvhhdRxMV+1XEUDY/u53Ef+O0AeazfFf4gXovfF9gsUfh20ulgNs0YK4bOATjcT0yQeCRW3d/FH4i+E5re/8R6FZ3Oj3G10mtQQpVgCNrgnBwejCu2+EuiWD/CHS7W4toriC8R5Z45UDK5LnqD16D8q6HxxLpth4C1k36RLZLZumxlG3O3CqB65xigCz4V8U6b4w0KHVtLlLRP8ro3DROOqsOxFbVeJfs2xzL4b1qRg3kNdoEJ6FgnzfzWvbaAOa8TePfDnhC6trbW7420lwjPGPKZ8gcdgcVx+qftAeDbJT9jN7qD9hFDsX83x/Kuj+JdnpEvhOa81Xw7Jri2rK0dvCCJMk4yCOQOea8Bs9cujdNH4X+GFjHKGIDTWkt3Iv4vwD+FAHdL8c/EuuzGLwz4KlnzwrOXl/PaAP1rhPip4h8c30Wn6f4ut7WyV83MVtAAD3UFsE++M+9drYR/HPVVVI/I0mDsHjhiCj/dAJ/SvNvilZeJLPxZBZ+JdTTU9QFqhWSJcAKS2FHAzznt3oAv+CPHPij4aW9vNLYyXGg3/AO9SOXIRucFo37NxyPbp3r3zwv8AFzwl4oVI49QWyu2x/o15iNs+gP3W/A1qaBoNjp3w/wBM0fVILd7eCzjSdLhQU3bctnPHUmvCfiTpvwmsxN/Y13cDUucQ6awkhDf7RbgD/dP4UAfTYIIBByDS15R8AzrzeCZW1ZpjZGb/AEDzgd2zHzEE87c9Pxr1egApGUMCGAIPY0tc54v8b6J4K043Wq3IEjA+TbJzJKf9kenueKANbUdRsND0ye/vp4rW0gXc8jcAD+p9q8Xs4NQ+N3itNQvIpbbwXpsp8mFuDdOPX69/QcdSar6ZpfiT43atHqmu+Zp3hKCTMFpGSPOx6HufV+3QV7rY2NrpljDZWUEcFtAgSOKMYVQOwoAmjjSKNY41CogCqqjAAHQCnUUUAFeY/GHxj4m8F2ml3+ixQmyaYrdSSR7+eNqn0B+bmvTqwPGo0RvB+pr4hdU0toSJmPUem3/azjHvQBS0v4h6Bf8AgiPxTPeR2tltxMJDzHIOqY6k56AdRzWl4W8T2Hi7RV1bTVuFtnkZF8+PYTtPUD0r5A8IR6XfeLdN0vWbydNCkvMspOASeFLDOBngE9ga+1La3htLaO3t4kihjUKkaDCqB0AFAHPeIvH/AIc8Lajaafqt+Iru6K7IkQuQCcBmx0Ga6auW8RfD3w54o1ez1XU7NmvLUqUkjkK7gpyFbHUZrqaAOdvPHXhmw8RR6BdavbxanIQBC2eCRkAt0BOeATXREgDJ6V4p8aPhcNWivPFuluwvoIVae2VOJVXq4PXcFx9cVy8nxXn1D4HXdhNdEa3HIliz5+aSFsnf9dqlSf8AGgD6JsdTsNTSR7C8gulikMbmGQOFYdQcd6tVwHwa0GHRPhtprrHtuL5ftUzd2Lfd/JcV39ABRRRQAUVzWqfEDwpousf2TqOt2tveBdzI7cJxnDHop9jU9j428L6lMsNn4g02aVuFRLlcn6DNAG9RRWJ4t10eHPDlzfgAygbIVPdzwP8AH8KunCVSahHdgZ/ivx7pvhf9wVN1fEZECNjaPVj2/nWRo/xX0643prFs+nyqRt4LhgfwyMVgeB/D8dzaXnjDXB9o2b5YVl5DsuSXPrzwK5TQ9MvfGvizEhZjNJ51zJ2RM8/4CvoaeAwfLOEvsbyv17JFWR9FqwdQynIIyDS01EWONUUYVQAB7UPIka7pHVV9WOBXznoScP8AEP4Y2PxBW0e4v7izuLUMsbxgOpBxnKn6dc15u/wA8R6axk0XxcqsvK5EkJ/NSa+gQQQCCCD0IrkPGXxI0PwaogndrvU5OIrC2+aViemf7o+v4A0gPL7bxJ8R/hjq2nQ+LJV1PRrqYQCUyCRhn+633sgc4I5xX0ADkZryPRPB/iHxz4htfFPjlBa2tq2/T9GXoncM/v0PPJxzgcV65QAV4jpvxvutM8fajoXiy0jtrJbt4op0Qq0ABwu8d1Iwc++ele3V89ftEweHPtdjJG+PEbAB44gDuh7GT0Pp3P0oA9M8WfFjwz4SuorOeaW8vJVDCCyUSEA/dyc4GcjFdpazrdWkNwqOiyorhZFwwBGcEdjXyd8HNT8OaZ44RvEsOLg4S0uJ2/dwSDj5geh7Anp+o+tQQwBBBB5BFAGF41tNTv8AwXq9rozMuoy27LAVfac+gPYkZFeS/s86Olvd69d3dtfRapCwt5DMMJtJyRjGd4K859q94pAAM4HWgBaKKjuLiG0t5Li4lSKGJS7yOcKqjqSfSgCSiuSf4oeCI2Kt4m0/I9JM/wAhTNJ+KHhLXdfh0bS9TN1dzBimyF9vAyfmIx0FAHYVwfxPP/EtsB/02b/0Gu8rg/if/wAg/Tx/01b/ANBoO3Lv95h/XQ6o6laaZo1vc3s6QxCJOW7nA4A7motK8S6VrMzQ2dzulUZ2MpUkeoz1rg9OtrjxvrieezrptmirgHsBjA9zjP0qfX7K38MeMNKudPjEMT7cop467W/MGg6PqVK7pSb9o1fyXkemVwnxD8Da14vezbSvE9zpCwKweKPdtkJPU7WBzXd1geJvGmgeEbXz9Z1COBiMpCPmkf6KOfx6UHlHlsXwN8SM4N18QL0j/YEhP6vWH4x8Kf8ACA2IlT4i6xNrLY+yWMRYvI+eMgOSB7/zrr18SePviN8vhqy/4R3RH4/tK8GZpB6oP8Pzrp/Cfwv0Pwxc/wBoyGXU9YY7nv7w73z6qD93+fvQB0HhU6o3hXSzrf8AyEzbJ9pzjO/HOcd/WteisXxP4p0nwjo8mpatciKIcIg5eVv7qjuaAOLt/iNNH8XNe0TULy2t9E06y83dIoUq4CEkt/wI8fSsab443us6/wD2V4L8NS6qckebKxTd/tYA+VfdiK8U8U6o3irxTP4nu9PuLDSb+6EbPEpbhQAcE8M+0A46Zr6v8E6D4e0Pw3bL4bjjNnOgkFwp3NPkfeZu5/lQBv27SvbRPPGI5mQF0DZCtjkZ74NVNY0XTvEGmyadqtpHdWkhBaN84JByDxyDV+igDE0Lwf4e8NFjo2kWto7DBkRMuR6bjk4/GtuiigAorjPG/wATdC8ByQwakLmW6mj8yOGCPJK5xkkkAd64yL9ofTJm/deGtXdfVNpoA9morC8J+J4fFujf2lBZXtmnmGPy7uLY2Rjkeo561R+I/if/AIRHwNqOqRttudnlW3/XRuAfw5P4UAYniv4l3+neIZdA8NeG7nW9Qt0ElwUOI41I45Gcn8vStH4e/ES08dWlzGbV7HU7Mhbq0kOSvbI74yCORkGuL+E/w51PSL3SvFkt+R9uspHvYHLb3aQlkPp02k571m6Ffppvx/8AGl5AAIIbSVpAOhceX/NqcYuTSXUTaSuzvvGF5PretW3hmxbqwa4YdB3wfoOfyrtNPsYNNsYbO2TbFEu0e/ufc1yPw+09pILrXLr57m7dgrH+7nk/if5VneNvjHpXgzXG0d9Nvry8RFdxEAqjIyOT149BXbjJKFsPDaO/m+v+Rz4dOV6st3+R6RXk3xg+Juq+Bb/SbTSobdnnVppjOhYFQQAo54zzz9Kq2fx6+15K+C9aYA8GAeZ/QV5h8Y/GI8X6jpcv9iX+mPbwupF7HsZwWGMewwfzrhOk+o9E1SLW9CsNUhGI7uBJgM5xuGcfhUGreG9L1mMi6tU8ztKg2uPx/wAa4b4P+OdC1XwhpmiR3aw6lZQLC9vMwVnx/En94H25Fen1UJyg+aLsyZRUlaSPMlOo/D7V0R5GuNInb8Pfjsw/WvSopUmiSWNgyOoZSO4PSud8eQxy+ErtnAJjKupPY7gP6mrHg12k8JaczkkiMjJ9ASBXfiWq9CNdr3r2fnpe5z0l7Oo6S2tdG7RRRXnHUFFFFABRRRQAUUUUAZXiTxFp/hbQrjV9Tl8u3hHQfeduyqO5NeAQJ4t+O+us00jad4at5OQv3F9h/fkx3PA9q7b9oPR9U1PwhZXFhDLNb2dwZLlI+cKVwGI7gc/TNcN4H+OsXhfw9aaNdeHkeG2TastrLsLepYEHJPc5oA9+8M+FtI8I6UmnaRarDEOXc8vI395j3NbNeKL+0loBHzaJqYPs0Z/rUVx+0npCofs2gX0j9hJKiD9M0Ae4UV806h+0f4gmyLHSNPth2MheU/zAqvZfGr4kXUivBYQXSE/cjsGIP4g0AfT1FZHhfV7nXvDdlqd5p0un3Fwm57aX7yHJHfnBxkfWtegDmPiHrV54f8Bavqenrm6hg/dn+4SQu78M5/CuC+BfgQ6bpbeK9TQtqOoAm3MnLRxH+L6t1+mPWvYnRZEZHUMrDBVhkEUqqFUKoAAGAB2oA+cPiv4U1HwJ4zg8ceH1MdtJOJXKLxDN3BH91+fzI9K9f+Hnj+18f6RNdwWc1rLbOscyPgruIz8pHUfrXW3FvDd28lvcRJNDINrxyKGVh6EHrUVjp9npdqtrYWsNrbp92KFAij8BQBZoorw6+17VfH/xst9B069mtNH0OYyzNC5UyMhG4kjrlsKB6ZoA9xorLsvEWkahrF5pNpfRTX1kAbiFDkx56ZPT/CtSgDzj46Z/4VTqWP8AnrD/AOjBR8CyD8KdN/66zf8Aow1tfE3RZtf+HWs2FvGZLgw+ZEo6syENge5xivM/gX4/0XTvDz+HNWvYrK5iuGeAzttWRWxkZPAIOeD60Ae80U2ORJo1kidXRuQynIP406gDwn4v+CdU0rxFb+PvDEbm4hZXukiXJVl6SY7gjhv/AK5rJ8WfG218UfDq/wBKGnXNpqVwiRyHhovvDdg9egPBFfRleAfHBl1jxd4c8HafGivNKJZhGoHzSNtBOPQBj+NAHpXwl0v+yfhjokJXa8sJuH+rkt/Iiu1qG1t47S0htoRiOFFjQegAwP5VyfxC+Idj4A063mntpbu7umK29vGcbiMZJPYcjsaAOyoqppd5JqGlWl5LayWsk8KyNBJ96MkZ2n3FW6ACiiigDM8RwXFz4b1KC1BM8ltIqAdScHivGPDnizUtA0afRdM0tzqM0xJlKkspwAAEx1GO9e9U0RoHLhFDHvjmu/C4yNGnKnOHMm7721X5jTPG/wDhVetXmkHUZrtRq0rmRoJG6g88t2bP4UttN8T9LUW6Q3UqJwPMRJRj/er2Witv7WqyVqsYyXS629Auc34RufE9zaynxHaQQMMeUUwGbrncASB2rpKKK86rP2k3JJLyWwiF7S2kfe9vEzepQE1KqqgwqhR6AYpaKzAKjuGZLaV0Te6oSq/3jjgVJRQCPJIPGHih7qZE3yzSDaI/Iz5Z9hjr9a1tJ8Fajqt4NQ8RSvgnPlM2Xf2P90ewr0QKASQBk9TS0HozzDT9zBRv1W5n6tpaal4fvdKUrElxbPbghchAykDj2rnfhv4Dh8BeHTYmSKe9lkL3Fyibd/8AdHPOAP612VFB5xR1bRtN12xay1Wygu7Zuscybhn1HofcVm+G/BPh7wi1y2iaclq1zjzWDsxIHQZYnA5NdBRQB5v4x+Lll4e8Q23h/SrCTWNWeVUlhhbAjz/DnBy/t271r+LviZ4b8H2jm8vEmvdvyWUDB5CfQ44Ue5rzXxb8Cdc1Dxbfaxo2s2yJeTPMRMXjeMsckZUHI5PpW34I+A+m6DepqWv3KapdxtujhCEQofUg8sfrx7UAei+EdYvte8L2Oqajp/2C4uU8w2+/dtUn5TyAeRg49626OlQXl5bafZy3d3PHBbwqXklkbaqgdyaAOD+NPiBdC+HF9Gr4uL/FpEM8/N94/wDfIP51b+EWhtoXw00mGRSs1whupARzlzkf+O7a8m1HUJfjX8VrKxs1k/4R7TjuZiCAYwcs59C5AUe3419HoixoqIoVVAAA6AUAeS/GP4d+JfGtzYTaPeRPbQIVazmk2APk/OOxOMDn0ri9F/Z/8S3qxw67rENnYo2fIgcyn8BwoPvzX0hRQBzvhDwTongnTjaaRb7WcDzp3OZJiM4LH8TwOKyvix4WvPFvgO6sNPUPeRSJcQx5xvK9V+pBOPeu3ooA+XvC/wAZtb8CaJD4dvtBSb7JuWPzWaGRQSTgjBzyTSz3Hj344alFAtv9j0WNwxIUrBH7knmRvb+VfTM1na3LBp7aGUjoXQN/OpURY0CIoVR0CjAFAGR4W8NWPhLw7a6NYA+TAvzOw+aRz95j7k1s0UUAFGKKKACvnb4u+MfsnxDS2t/C9pNqVgiC2vblHkZsjcCqA7WAJ4znnNfRNRtBE8qytEhkUYVyoyPoaAPmGDwX8UfiTKs+sz3MFm5yGv3MSAf7MQ/wr07wl8CfDegPHdamW1e8TBHnLthU+yd/xJr1SigBFVURURQqqMAAYAFLRRQBwHxZ8fXHgTw5FNYwCS+vHMUDuMpGQMlj6n0H+FfNOmeI9PvPEb614ziv9bkLBvJEoVXPYMT/AA/7IAFfZOqaTp+tWL2Wp2cN3bP1jmQMPr7H3rlm+EfgNjk+G7X8Gcf+zUAebN+0hZ20CQ2HhVkjRQqIbkKqgdAAF4FU2/aV1DPy+G7YD3um/wDia9ai+FfgaE/L4ZsT/vqW/ma04PBXha2AEPh3Slx/06J/hQB4/p37SiNcKup+HSkJ6vb3O5h+BAz+de0+H/EOm+J9Gh1XSp/OtZc4JGGUjqGHY02XwvoE9u8Emiac0TjDIbVMH9KTQPC+jeF4LiDRrFLSK4lM0iISQWxjjPQYHQUAXdS1C20nTbnULyTy7a2iaWVsZwqjJ4r53u7nxH8ePEy21oklh4YtJMl2HCj+8f70hHQdv1r6PlijmieKVFkjcFWRhkMD1BFRWNhZ6Zara2NrDbW6fdihQIo/AUAeSfEn4QWFx4Jtf+EctBFe6REfLRfvXEfVgT3bOWB9cjvXc/Da91HUPh7o0+qpIl55Ox/MUqx2kqCQe5ABrqqKAGLLG7OqOrMhwwByVPv6U+vmVPCPxPfxbrtjYQXljb6rdu1zeFgqNHubBD+mG6Dk9K+jdH04aRo1npwuJrj7NCsXnTNud8DGSfWgC4yhlKsAQRgg96+PfiF4L/sL4myaFYEGG9lR7ZFOSqyNgKR7HI+mK+wJZUgheWVgkaKWZj0AHJNfOXgISfEX453viSVS1lZOZ0z0AHyRD+v4GgD6IsLSOw0+2s4v9XbxLEv0UAD+VWCQBkmiuG+KvhbWvFvhJdP0O5SG4S4WVkdyglUA/LuHTkg88cUAdzRWR4Xt9VtPC+m2+tyRyalFAqTtGcgsPfucYyfWtegDzPxX8EfDninXZ9Xkub60uLg7phC6lWbAGcMDjpVDTv2evCNncJLc3Go3gU58uSVVU/XaAf1r1uigBERURUUYVRgD2rifiho9/q/hyFdPhkneG4DtFGMkrgjOO+M129FbYes6NVVI7oDw6x0nxn4msrTRjDLZaVAoT50MSYHc92NZckHiP4e63I8QeInKCXZuimTP5fh1FfQtNdFkXa6hlPYjIr1I5zJNp01yPp+pVzwpviZ4tvR5EEkYduB5Ftlvw61raP4E8ReJrlL3xLeXEVvnPlyuTI49AOij/OK9djt4YjmOKND6qoFSVE80UVbD01B9+v5CueL+MPiDqTa5F4A8Aw/6dGBby3bHiDAwQpPoOrH8Oa63wL8MtM8Jf8TC6f8AtPXZfmmv5/mIY9dmeg9+prz/AMYfAPUr7xBe6roOrwqt1K0zRXRZWVmOSAwByM+tc5/wo74hqcLqFpgel8/+FeS227sR9PUV8zw/BL4iqw/4nNtF7i+l/oK7nwR8M/Guga1Be6l4zke1Rw0lpHJJKJgM/Kd/AH4UgPXmyFO0AtjgE1434G+E15L4ou/FnjVRLftctJBaM4kVTnh2PfH8I7Yr2WigDyD4mfBWDxRcyaxoMkVpqj8zQvxHOfXI+63v0P61j/C/Tvil4d8RWmlanaz/ANggt532mRZFjUA/cYEkc4wOle70UAY+s+KdE8PT2cGq6hFbS3kgjgR8kufw6D3PFaryxxsiu6qXOFBONx9B618qfEB/FMPxdvNSk0u4uJoLj/QFktmkj2L/AKsqBw2OD9eteg/D74feKNW8Q2/i3x1eXDSwt5ttZzOdwfsxAOEA7L+dAHttQXtnbajZT2d3Cs1tOhjljccMpGCDU9FAHmqfAnwMmoG6Nlcsm7cLdrhvLHtjrj8a7nS9A0jRIhHpmm2looGP3MQU/mOTWjRQAVheKPDo8RWMUInEMkUm9WK5HTBGKpeM/E9x4fht0tYlM0+T5kgyqgf1rjbX4g61HciSeSO4QAjytgUE+uRzQejhcFiJJVqVl2PSdE0a20DTFtYDnGWkkbgufU1wmuXa+KvGtlZ2PzwwMFMg6HByx+gxiopLvxZ4vPkxxPDat97apjjx7seTXaeGfC9v4etyQwlu5B+8lxj8B6Cg1ssK5Vakr1H07X6s8Q8f/HbWTqV7pPh+AafFBK8LXMg3TMVJBIB4Xp7muR8MeL/CmlX51fXtJ1HxBq7Nu827mXYp9Qpzk+5J+gr6pu/Dmh38zTXmj6fcStyXltkZj9SRUC+D/DSnK+H9KB/684/8KDyTyUftJ6YoAHhu6AHAxcr/AIVds/2jdCmkAudE1OFO7pskA/UV6tHoGjQ/6rSLBP8AdtkH9KsrY2ioUW1hCnsIxj+VAGVoXjDRPE2kS6lo16l1FEpMiKCHQ4zhl6g8V4x4d8I6v8Y9ek8U+KZ5INEjlaO1s0JBZQfur/dX1bqTmvXtM8CaJo3iu58Q6bC9rcXMPlSwQtthbnO7YB14rpVUKMKAB6CgDnta8E6HrfhNvDclnHDYBAIVhUL5JHRl9x+vOeteWeEdA+Inw58X2uiW0Z1Xw5cTZdx/q40JwWyf9Ww645Br3WigDP12bUrfQb6bSIEn1FIGa3ikPys+OAa8m+FHjXxt4h8X3mm+InCw28LSyQy2flOpJAABAGPoe1b3xA8d6rp3ivRPCXhsQ/2rfSI8skq7ljjJPGPcAknsB716SoAOcDcRyRQA6iiigDk/H/iPTvCWiR6vfaO+pMJRDGscSsVLZPJI+UcfyrziD4s+N9Vb/infh64h/hZ43YY+oCivcioYYIBHoaWgDlvBOqeK9UsriTxTotvpkqsvkrFJuLjHORk4xx3rzn48zvqOteEvDQJEV3db5OeuWVB/Nq9vrzT4rfDvUvGDaXqmh3kcGq6a5MYlO1WBIOQcHBBA9qAOq8U+JdN8EeF5dQumVY4I9kEIPMjgYVFH+cCvEvA2lX7+DvEPjDUFYT6xcqiEj7yb9zsPYtgf8BrZ0/4PeK/FGsw33xB1z7RbwfdtopSxYf3eAFQHvjmvYrzQ7O50BtHjiWC1EQjjWMYEYH3cD2wK2w81TqxnLZNGdWLlBxXUxoE1Nfhmi+H/ACv7TNl/o3mHC+Yf69evevI9D+CvibxTJcah4y1q+s7lZSiozCZ3Xj5g27AHt7V6Boeu3XhC8bRNbRhbAkxTAZCg9x6qf0r0O3uIbqBZreVJImGVdDkGtcZQlTqOT1T1T7kUKkZRUeq6Hl0HwG0KGMKdb10sB1W5C/oBXn3xL0iDVPiZ4Z8HafJcXLWdvDaSySvvfBYsST6hTk19LHpwM18r22h/EfU/Hes6lo2k31jeXc8ivdTqEMKFugkYAdABlecdK5Dc7/4jfD34cWrG/u9WXw9eH5gLdgxc+vldc+4xXn3hHxP4yi8U2+n+EdY1TXLRZVDJdQnyymed2WbYMd8ivQfDXwBtRONQ8X6lLql2x3NDG7BCf9pz8zfpXe3+r6B4H077DplpbRSgfJa26hQD6tj+vNaU6U6suWCuyZzjBXkyr8RdS26bBpMXzXN06kovXaDx+Zx+VdTo9j/ZujWln3hiVT9e/wCtch4V0K81PUz4j1rJkY7oI2GPo2OwHYV3tdeLlGnCOHi721b8/wDgGFBOUnVl129AooorgOkKKKKACiiigAooooACARgjIrjtX+FfgvW5nnu9Bt1mc5aSAmIk+p2kCuxooA8yb4CeBSeLS8X2F21SW/wJ8BwSB20+4mx/DJdOR+hFek1Xe3lZywupVB6KAuB+lAGNpngXwro+PsGgafEw6N5AZv8Avo5Nb6IsahUUKo6ADAFV/s03/P7N+S/4UfZpv+f2b8l/woAtUVV+zTf8/s35L/hR9mm/5/ZvyX/CgC1RVX7NN/z+zfkv+FH2ab/n9m/Jf8KALVFVfs03/P7N+S/4UfZpv+f2b8l/woAtV8+638LvHujeMtV1DwfeqLbVGcvKsyxuiu24qc+h6Ec17x9mm/5/ZvyX/Cj7NN/z+zfkv+FAHJ/DXwDH4F0OSOWYXOp3bCS7nHQnsozzgZPPcnNdtVX7NN/z+zfkv+FH2ab/AJ/ZvyX/AAoAtV594g+DHg7xDqb6hNZzW08jbpfskuxZDnJJXBGT7Yrt/s03/P7N+S/4UfZpv+f2b8l/woAraBoOn+GdGg0nTImjtIM7FZyx5JJ5Pua06q/Zpv8An9m/Jf8ACj7NN/z+zfkv+FAFkkAEk4A714B8PwfHPx11rxO432mnlvIJ5H/POP8A8dDGvSfiZq03hr4fatfrezecYvJiGF++/wAo7dsk/hWB8DPDUml/D+K+MskU2pyGchQPuD5U6j0BP40AerV598Vvh2/jvSLd7GdYNUsWZ7dn4VwcZUnt0BB9q6vVruHRNKudTv8AUpYrW2jMkjlV4A/Dr2rnfAHi648d6beaitteWVpHcGK3eQofOUd/u8Ed+ooA6DwtBrFt4Y0+DX5o5tUjiC3EkZyGI6c9zjGT61sVV+zTf8/s35L/AIUfZpv+f2b8l/woAtUVV+zTf8/s35L/AIUfZpv+f2b8l/woAtUVV+zTf8/s35L/AIUfZpv+f2b8l/woAtUVV+zTf8/s35L/AIUfZpv+f2b8l/woAtUVV+zTf8/s35L/AIUfZpv+f2b8l/woAtUVV+zTf8/s35L/AIUfZpv+f2b8l/woAtUVV+zTf8/s35L/AIUfZpv+f2b8l/woAtUVV+zTf8/s35L/AIUfZpv+f2b8l/woAtUVV+zTf8/s35L/AIUfZpv+f2b8l/woAtUVV+zTf8/s35L/AIUfZpv+f2b8l/woAtUVV+zTf8/s35L/AIUfZpv+f2b8l/woA5j4n6zrWheBby80CF5L7cqBo497RKTguB3x/WvnrQfBvxB+IM8q3Et9HY3Eokubm+ZljZhxnafvEA8AD8q+rPs03/P7N+S/4UfZZv8An9m/Jf8ACgDE8FeCNK8DaMLDTlLyPhp7lx88zep9B6DtXS1V+zTf8/s35L/hR9mm/wCf2b8l/wAKALVFVfs03/P7N+S/4UfZpv8An9m/Jf8ACgC1RVX7NN/z+zfkv+FH2ab/AJ/ZvyX/AAoAtUVV+zTf8/s35L/hR9mm/wCf2b8l/wAKALVFVfs03/P7N+S/4UfZpv8An9m/Jf8ACgC1RVX7NN/z+zfkv+FH2ab/AJ/ZvyX/AAoAtUVV+zTf8/s35L/hR9mm/wCf2b8l/wAKALVFVfs03/P7N+S/4UfZpv8An9m/Jf8ACgC1RVX7NN/z+zfkv+FH2ab/AJ/ZvyX/AAoAtUVV+zTf8/s35L/hR9mm/wCf2b8l/wAKALVFVfs03/P7N+S/4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Fx0n19n8P8AhckMtlFkSzr2znn8Tx6CgCJrgfFT4yadeaVGW0Lw8Qz3hBCyuG3YX6kDHsCa9yrO0PQtN8OaVDpulWqW1rEOFXqT3JPUk+prRoAK8g+OXg+/1GzsvFOjeYb/AEnl1j+95YO4Mo9VPP0J9K9eJABJOAK+fvG/xG1zx5rzeDvAqSm3ZjHNdRnBmA4Y7v4Ix69/0oAst+0Qp8K2qW2lvP4jkHlvGQfKVum8Y5Of7o/OrPhX4Y634x1JPE3xGuZpiTug01jgAdtwHCj/AGRz61gav8ENe8J6VZa74c1KS61iz/eTxxLgg9cxeuOmD1/Su++GfxctfFxTSNXVbPXUGNv3UuCOu30b1X8qAPUFVUQKoAUDAA7CuR8f/D+08f6faW1zfXFo1rIZI3iAYZIxyp6119FAGR4X0NvDfh200lr+e+NupXz5/vMMkgewHQfSteiigAprukUbSSMERQWZmOAAOpNOqG7hguLOaC5VWt5I2SVWOAVIwc+2KAPPtT+OXgfTpXiS/mvHQ4P2WAsD9GOAa5//AIaO0BrkRxaLqjqWwG+TJ/DNQW2kfAvTZ5d13p87oeRNcySAfTsf1rRtfiZ8JtAkEWmxW8W3/lpa6cR+uAaAPTdD1eHXtFtdUt4Z4YrlN6x3CbHXnHI/CrV3dRWVnNdTuEhhjaR2PZQMk1DpOqWutaTa6nZOz211GJY2ZSpKn1B6VzvxRkki+GPiFos7vsjDj0JAP6E0AeVaHrvxA+K3iLUr3QtcOiaZZ4ESbcryTtUgD5mIGST0r3TS7W7i0e1g1WaK7vViAuJUjCrI+OSBXlP7OUtqfBuowxyIboXxaRM/MFKKFP04NewzTxW8LzTypFEgyzuwVVHqSelAHgf7QPg/S7CxsfEVhBFaXT3H2eZYlCiTIJDYHcY6+9er/DfVrjW/h5ol/dtuuJLcK7f3ipK5PucZrwr4ueMh8QvE+neHPDoN1bQS7I3TpPM3GR/sgd/qelfRPhnRk8PeGdN0iM7haW6RFv7zAcn8TmgDVooooAKKKKACo57iG1gee4mjhiQZaSRgqqPcnpUlcX8TJ/CcnhiXTPFeqfYra5IZRG+JWKkEFVAJPPtigCtrfxk8E6IWRtWF5KP+Wdkhl/8AHvu/rXC3v7Rb3Mvk6D4YnuHJ+UzyZJ/4CgP865OHWfg3oDg22h6rrcg/juiAn5EgfpXTaf8AHrRrCMRaT4HeGMcBbd0X/wBBSgATx38Zdbw2neF1tY26MbMr+sjV5j4/1TxdrGvNZeKZkkvdNjO6OMIBEDhj93jPIr2SH4++YRu8F6tj1Rt3/steJajr1tqmqeK9VuvMjutQyLaNlJI3SqSCegwi4oA6rTvB+vR+EtL+IHhRJ4L6AMLq3jUhm2HHmoP4lYfeX649vZfhr8VtP8b262V1stNbjX57cnCy46tHn9R1Fbfw1t3tvht4eikBDfYo2IPuM/1rgPit8LF2y+L/AAtmz1S0zcTxQ/KJMcl1x0cdff69QD2miuG+FPjaTxv4PS6utv8AaFs/kXO3gMwAIfHbIP55ruaACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAorD8XeKbLwd4budZvld44cKsadZHPCqPTJ7183658Y/E3i+4ayTUrfw/pshwxi3bgvu4BY/gBQB7B8S/ilZ+G7SXR9HcXviG4HlRRQ/P5BPGWx/F6L1zVz4R+CZ/BvhQ/b8/2pfv8AaLoE52cfKn1A6+5NeT+FvFHw1+Hw+22/2/xBrZHN0bfy1Q99gc8fXk1Pq/7R+sTbk0nRrS1Xs9w7St+QwKAPpGivly08dfGDxNJu0wXzJ2NvYqqf99FcfrXsvwzufH01rdR+NbRE24NvMSgkb1UqnGB1z70Ad9XPeMvGOmeCdDfU9SZjk7IYU+/K+MhR/j2roa5Xxd4C0rxrd6XLqslw0NhI0n2dHxHLnHDD8O3vQB8v+IdM1/xgNV8cx6B9l0xpQ0hhU7eeCwzy3+0w4ya97+Cnijw/qvhOLS9NtobC/tF/0m2U8yH/AJ6gnlge/p09K9JjtLeK0W0jgjW2VPLEQUbQuMYx0xjtXgvjn4Wat4T1yPxX4BSUeXJ5j2cPLRHPO0fxIe69vp0APoCuc8YeCNG8b6fFaaxFKRCxeKSJ9rISMHH/ANetqynll023nuk8mVoVeVG42MVBIPpg5rkPCHxItfGfijV9N02ykNhYKCt/u+WVs4xtxxnnHsKAOk8OaDaeGNAtNHsWla2tU2oZW3Mckkkn6ms7x14OtPG/hmfSrnCS/wCst5sZMUg6H6dj7Gt21v7O+M32S6hn8mQxS+U4bY46qcdD7VYoA+fPhL4zvfB3iCbwD4oJgUTFLZ5DxFIf4M/3W6g+p96dqkh8ZftL2dovz2ukFQQeQPKBdv8Ax84rsPjN4E0nXfDtz4gkmWy1HToTILkDiRBzsbHXnoexNcr+zroksz6z4nu9zySMLWOR+Sx+85z/AN80Ae90UVx/j/4hWHw/sbO4vLWa6e6lKJFEwBAAyzc+nH50AdhRVbT7+31TTre+tJFlt7iNZI3U5BUjIqzQBy3xAsfE+oeGHh8J3q2upCVW3FgpdOcqGPQ9Pyryu10X47t5du2qLDGvHmSzQtge5AJNe+0UAef+EvB3i/TNWi1HxD41uNRCKwayRMRNkY5J9OvQV6BRRQAV4/8AHXwZPqelW/ifS0b+0NK5k8v7zRZzuHup5+hNewUhAZSrAEHgg0AeY+DvjN4d1TwrHc6zqUFlqdvHi5hkO0yMB95PXPoOh4rlfhRZ3Pjv4h6v4+1OM+VC5js0bkKxGAB/upj8WrB+N3hfwzo2rafZ6FpvlaxqL73jic7ApOBhOgLMe3HFe7+CPDUXhLwhp+jxgb4YwZmH8Uh5Y/n+gFAHQUUVm+INTl0bw9qGpQWr3UtrbvKkCdXIGcUASavrOnaDp8l/ql5Fa20Y5kkbH4D1PsK8S1DxBr3xs1h9D0BZtO8LQsPtl24w0o9D9eyfia8c17xfqvirWlv/ABBPLdqrZFusnlog/uqOdv5Zrrrb41+INOsItL8O6Xpel2kYxHHBAZG+uWJyfcigD6c8PeH9O8MaLBpWlwCK2hH1Zz3Zj3J9a1M18nL4l+L3iJibeXXXVv8An2tzEv5qoqxb+BPjBqT7nbVo8/xT6js/9noA+qaK8l+GfhT4jeH9XDeINZjn0pkIe3luGnfOPlKkjjnrzXrVAHjnxo+IE9ii+DtCVpdVv1CTlF3FEbgIB/eb9B9a4Gf4S+OfBFvY+IdDuDJexxb7iO1bEkB7rg8OuOv48V7LpPw0trL4k6r4wvLhbqa5bdaxGPHkEjDHOeTgYB+tdtd2sF9ZzWlzGJIJkaORD0ZSMEflQB5h8IfifqHjh7vTdVskS7tIhIbmEYRxnGCvZvp1wa9VrH8P+FdD8LW7waLp0NmkmPMKAlnx0yTya1pJEhjaSV1RFGWZjgAepNAGbe+G9E1K/ivr3SbK4u4iCk0sCs6kdOcZ4rUpkU0c8SywyJJG4yrowIYeoIp9ABXK+OvAem+PdMgs9RnuYRbyGSN4GAOSMcggg11VFAHyZpnw80y4+NE3g17m6awhZwZgVEh2x7vTHXjpX0v4U8Kaf4P0g6bpr3LQmQyE3EpdskAfgOOlcBpvgXWLH4/3fiMwFtJmheVbgEABmQLsIznOc163QAVjeK7eW68L6hDAheRoshVGScEGtmornzvssv2cgTbD5e7puxx+tBdOXLNSXRnBfD/w7JFM+qXsRRwNsCOMH3bH6D8a6TxR4jg0LTnw4a8kUiGPPOf7x9hXnljJ4os9Qnlis7t7+UFGlkjZiBnnGeOtdFovge6ur0aj4hlMrk7vJLbix/2j6ewpnsYmlD2vtq8012XXy9C98PNLls9JmvZwQ94wZQeu0dD+JJNdjSABQAAABwAKWkeTXqutUdR9Tyr4467rNjoOn6Joschn1mZrdmi++QAPkX/ezjPoDW18MPh3beBNCHmhJNWulDXUw7eiL/sj9TzXctGjlS6KxU5UkZwfUU6gyPHfiz8KJtauP+Em8MqY9ZiIeaGM7TPjoyns4/X612nw21PxBqvg23m8TWUlrqKM0Z81NjSqMYcr2J/pnvXXUUAFfKvjzxoPGnxAl0zWtUl03w3ZXLxBIo2YnYSCxUdXOOp4H8/qquH8T/CXwl4r1B7+9spIbx/vzWsnllz6kdCffGaAOf0T4s/DHw5pEGmaXdyQWsIwqraSZJ7knHJPc1Zl+PngaP7txfS/7lqf64qt/wAM9eDf+e+q/wDgQv8A8TU0XwA8ER/fj1CX/fusfyAoAg/4aF8G7seRquPX7Ov/AMVXXeE/iN4a8Zu0Wk3xNyq7mtpkKSAeuD1/AmsVfgd4BVcf2RI3ubqTP86yv+FGadpfifTdb8N6pPp7WtwkjwyjzVKg8hTkEZHHOaAPWq8X+K/xF1ddbHgfwnDMdUnCrPNGPnG4ZCJ6cHJbtXtFc/YeDNG07xXqHiWCFzqV8oWR3fcFHGdo7ZwM0Acf8NfhDZeE4l1LWBHfa3IuWZhuSDPULnqfVvyrifiJ8PdR8Ba0vjbwYXit4n8yaCP/AJYZ68d4z0I7Z9On0NSModSrAFSMEEdaAMzw5qr654b03VJIhC93bJM0YzhSwyRzVgarp51U6WL23+3iPzTbeYPM2f3tvXFWXykR2LkgcL/SvlHwr8RoPDvjfXPEuv2N1faxPujhQMFERLfMCTyMAADjpQB9Y0VxPw113xL4m0i51nXbWC0trmQNYQIpDLH3JJ6g9vx9q7agAooooAKKKKACiiigDnvEviLSNLjFpqMZuTKuTAqBuPU54rCf4kafbWiR2OmyDaMKjEIq+nSuk1jwrpeuXCXF5HJ5qrt3RvtyPQ0tj4U0TTyGhsIi4/jk+c/rQehTqYSNNKabf4HDtc+KPGreSifZ7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oAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKAP//Z"}}},{"cell_type":"markdown","source":"<div \n     style=\"padding: 20px; \n            color: #3b3b3b;\n            font-size: 115%;\n            text-align: left;\n            display: fill;\n            border-radius: 5px;\n            background-color: #d7ffd7;\n            overflow: hidden;\n            font-weight: 600;\n            border: 5px solid #ffcfa0;\"\n     >\n    And with this, we are done with our proof!\n</div>","metadata":{}},{"cell_type":"markdown","source":"<a id=\"3.3\"></a>\n## Note: x as a vector","metadata":{}},{"cell_type":"markdown","source":"<div \n     style=\"padding: 20px; \n            color: #3b3b3b;\n            font-size: 115%;\n            text-align: left;\n            display: fill;\n            border-radius: 5px;\n            background-color: #d7ffd7;\n            overflow: hidden;\n            font-weight: 600;\n            border: 5px solid #ffcfa0;\"\n     >\n    Small note: for simplicity's sake, in this notebook we've been going through the case where x is just one variable (scalar), and not a vector. <br>\n    The results above and the examples below can be generalized to x as a vector (which in practical terms means that we have multiple input variables), but we will not show these cases in this notebook.\n</div>","metadata":{}},{"cell_type":"markdown","source":"<a id=\"4\"></a>\n# 🧱 Fitting the Line","metadata":{}},{"cell_type":"markdown","source":"<div \n     style=\"padding: 20px; \n            color: #3b3b3b;\n            font-size: 115%;\n            text-align: left;\n            display: fill;\n            border-radius: 5px;\n            background-color: #d7ffd7;\n            overflow: hidden;\n            font-weight: 600;\n            border: 5px solid #ffcfa0;\"\n     >\n    Now it's time to build a Linear Regression model, using the results we gathered above.\n</div>","metadata":{}},{"cell_type":"markdown","source":"<a id=\"4.1\"></a>\n## Fit","metadata":{}},{"cell_type":"markdown","source":"<div \n     style=\"padding: 20px; \n            color: #3b3b3b;\n            font-size: 115%;\n            text-align: left;\n            display: fill;\n            border-radius: 5px;\n            background-color: #d7ffd7;\n            overflow: hidden;\n            font-weight: 600;\n            border: 5px solid #ffcfa0;\"\n     >\n    In our simplified dataset, the x variable will be the \"GrLivArea\" column, and the y variable will be the \"SalePrice\" column.\n</div>","metadata":{}},{"cell_type":"code","source":"df","metadata":{"execution":{"iopub.status.busy":"2022-10-16T16:32:02.142146Z","iopub.execute_input":"2022-10-16T16:32:02.143458Z","iopub.status.idle":"2022-10-16T16:32:02.165203Z","shell.execute_reply.started":"2022-10-16T16:32:02.143333Z","shell.execute_reply":"2022-10-16T16:32:02.163785Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"<div \n     style=\"padding: 20px; \n            color: #3b3b3b;\n            font-size: 115%;\n            text-align: left;\n            display: fill;\n            border-radius: 5px;\n            background-color: #d7ffd7;\n            overflow: hidden;\n            font-weight: 600;\n            border: 5px solid #ffcfa0;\"\n     >\n    Let's start by computing the means $ \\overline{x} = \\frac{1}{n}\\sum_{i=1}^n x_i $ and $ \\overline{y} = \\frac{1}{n}\\sum_{i=1}^n y_i $.\n</div>","metadata":{}},{"cell_type":"code","source":"x_mean = df['GrLivArea'].mean()\ny_mean = df['SalePrice'].mean()\n\nprint(f\"x_mean: {x_mean}\")\nprint(f\"y_mean: {y_mean}\")","metadata":{"execution":{"iopub.status.busy":"2022-10-16T16:32:02.167589Z","iopub.execute_input":"2022-10-16T16:32:02.168431Z","iopub.status.idle":"2022-10-16T16:32:02.179188Z","shell.execute_reply.started":"2022-10-16T16:32:02.168362Z","shell.execute_reply":"2022-10-16T16:32:02.177864Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"<div \n     style=\"padding: 20px; \n            color: #3b3b3b;\n            font-size: 115%;\n            text-align: left;\n            display: fill;\n            border-radius: 5px;\n            background-color: #d7ffd7;\n            overflow: hidden;\n            font-weight: 600;\n            border: 5px solid #ffcfa0;\"\n     >\n    To calculate $ b $, we'll also need these differences: $ (x_i - \\overline{x}) $ and $ (y_i - \\overline{y}) $, for each i-th row.\n</div>","metadata":{}},{"cell_type":"code","source":"df['x_diff'] = df['GrLivArea'] - x_mean\ndf['y_diff'] = df['SalePrice'] - y_mean\n\ndf","metadata":{"execution":{"iopub.status.busy":"2022-10-16T16:32:02.180947Z","iopub.execute_input":"2022-10-16T16:32:02.181679Z","iopub.status.idle":"2022-10-16T16:32:02.203918Z","shell.execute_reply.started":"2022-10-16T16:32:02.181633Z","shell.execute_reply":"2022-10-16T16:32:02.202245Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"<div \n     style=\"padding: 20px; \n            color: #3b3b3b;\n            font-size: 115%;\n            text-align: left;\n            display: fill;\n            border-radius: 5px;\n            background-color: #d7ffd7;\n            overflow: hidden;\n            font-weight: 600;\n            border: 5px solid #ffcfa0;\"\n     >\n    At this point we can easily compute:\n    $$ b = \\frac{\\sum_{i=1}^n (y_i - \\overline{y}) (x_i - \\overline{x})}{\\sum_{i=1}^n (x_i - \\overline{x})^2} $$\n    <br>\n    $$ a = \\overline{y} - b \\overline{x} $$\n</div>","metadata":{}},{"cell_type":"code","source":"b = (df['y_diff'] * df['x_diff']).sum() / (df['x_diff']**2).sum()\n\na = y_mean - b * x_mean\n\nprint(f\"a: {a}\")\nprint(f\"b: {b}\")","metadata":{"execution":{"iopub.status.busy":"2022-10-16T16:32:02.208424Z","iopub.execute_input":"2022-10-16T16:32:02.209151Z","iopub.status.idle":"2022-10-16T16:32:02.218055Z","shell.execute_reply.started":"2022-10-16T16:32:02.209111Z","shell.execute_reply":"2022-10-16T16:32:02.216582Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"<div \n     style=\"padding: 20px; \n            color: #3b3b3b;\n            font-size: 115%;\n            text-align: left;\n            display: fill;\n            border-radius: 5px;\n            background-color: #d7ffd7;\n            overflow: hidden;\n            font-weight: 600;\n            border: 5px solid #ffcfa0;\"\n     >\n    And now our Linear Regression prediction $ \\hat{y}_i $ is simply:\n    $$ \\hat{y}_i = a + b x_i $$\n</div>","metadata":{}},{"cell_type":"code","source":"df['y_pred'] = a + b * df['GrLivArea']\n\ndf","metadata":{"execution":{"iopub.status.busy":"2022-10-16T16:32:02.219932Z","iopub.execute_input":"2022-10-16T16:32:02.220399Z","iopub.status.idle":"2022-10-16T16:32:02.243262Z","shell.execute_reply.started":"2022-10-16T16:32:02.220335Z","shell.execute_reply":"2022-10-16T16:32:02.242443Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"<div \n     style=\"padding: 20px; \n            color: #3b3b3b;\n            font-size: 115%;\n            text-align: left;\n            display: fill;\n            border-radius: 5px;\n            background-color: #d7ffd7;\n            overflow: hidden;\n            font-weight: 600;\n            border: 5px solid #ffcfa0;\"\n     >\n    And with this we are done! Easy. <br>\n    Let's plot the data points and the Regression Line.\n</div>","metadata":{}},{"cell_type":"code","source":"plt.figure(figsize=(20, 10))\n\nplt.scatter(df['GrLivArea'], df['SalePrice'])\nplt.plot(df['GrLivArea'], df['y_pred'], color='lightgreen')\n\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2022-10-16T16:32:02.244472Z","iopub.execute_input":"2022-10-16T16:32:02.244760Z","iopub.status.idle":"2022-10-16T16:32:02.510496Z","shell.execute_reply.started":"2022-10-16T16:32:02.244733Z","shell.execute_reply":"2022-10-16T16:32:02.509384Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"<a id=\"4.2\"></a>\n## Evaluation: $ R^2 $","metadata":{}},{"cell_type":"markdown","source":"<div \n     style=\"padding: 20px; \n            color: #3b3b3b;\n            font-size: 115%;\n            text-align: left;\n            display: fill;\n            border-radius: 5px;\n            background-color: #d7ffd7;\n            overflow: hidden;\n            font-weight: 600;\n            border: 5px solid #ffcfa0;\"\n     >\n    There are a lot of ways we can evaluate models. One of the popular ones is the <u>Coefficient of Determination</u> or $ R^2 $. <br>\n    <br>\n    $$ R^2 = 1 - \\frac{\\sum_{i=1}^n (y_i - \\hat{y}_i)^2}{\\sum_{i=1}^n (y_i - \\overline{y})^2} $$\n</div>","metadata":{}},{"cell_type":"markdown","source":"<div \n     style=\"padding: 20px; \n            color: #3b3b3b;\n            font-size: 115%;\n            text-align: left;\n            display: fill;\n            border-radius: 5px;\n            background-color: #d7ffd7;\n            overflow: hidden;\n            font-weight: 600;\n            border: 5px solid #ffcfa0;\"\n     >\n    In the fraction, the numerator is the Sum of Squared Residuals of our model's prediction $ \\hat{y}_i $. <br>\n    The denominator is the Sum of Squared Residuals of the mean, which is proportional to the variable's variance. <br>\n    A perfect model will have a $ R^2 = 1 $. <br>\n    A model that always predicts the mean will have a $ R^2 = 0 $. <br>\n    $ R^2 $ can theoretically go to $ -\\infty $, because there is no limit to how bad a model can be.\n</div>","metadata":{}},{"cell_type":"code","source":"r_squared = 1 - ((df['y_pred'] - df['SalePrice'])**2).sum() / (df['y_diff']**2).sum()\nprint(f\"R Squared: {r_squared}\")","metadata":{"execution":{"iopub.status.busy":"2022-10-16T16:32:02.512679Z","iopub.execute_input":"2022-10-16T16:32:02.513511Z","iopub.status.idle":"2022-10-16T16:32:02.522946Z","shell.execute_reply.started":"2022-10-16T16:32:02.513462Z","shell.execute_reply":"2022-10-16T16:32:02.521718Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"<a id=\"5\"></a>\n# 💻 Code Implementation","metadata":{}},{"cell_type":"markdown","source":"<div \n     style=\"padding: 20px; \n            color: #3b3b3b;\n            font-size: 115%;\n            text-align: left;\n            display: fill;\n            border-radius: 5px;\n            background-color: #d7ffd7;\n            overflow: hidden;\n            font-weight: 600;\n            border: 5px solid #ffcfa0;\"\n     >\n    sklearn's LinearRegression class does all the heavy lifting we did above for us. Thank you open source community!\n</div>","metadata":{}},{"cell_type":"code","source":"# the class requires inputs to be 2D arrays, so we'll reshape them (pandas DataFrame columns are 1D arrays)\nsamples = df.shape[0]\nX = df['GrLivArea'].values.reshape(samples, 1)\ny = df['SalePrice'].values.reshape(samples, 1)","metadata":{"execution":{"iopub.status.busy":"2022-10-16T16:32:02.525235Z","iopub.execute_input":"2022-10-16T16:32:02.525777Z","iopub.status.idle":"2022-10-16T16:32:02.535104Z","shell.execute_reply.started":"2022-10-16T16:32:02.525652Z","shell.execute_reply":"2022-10-16T16:32:02.533679Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# instantiate model and fit on data\nmodel = LinearRegression()\n\nmodel.fit(X=X, y=y)","metadata":{"execution":{"iopub.status.busy":"2022-10-16T16:32:02.537211Z","iopub.execute_input":"2022-10-16T16:32:02.537860Z","iopub.status.idle":"2022-10-16T16:32:02.561168Z","shell.execute_reply.started":"2022-10-16T16:32:02.537813Z","shell.execute_reply":"2022-10-16T16:32:02.559792Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# plot the Regression Line and the raw data\nplt.figure(figsize=(20, 10))\n\nplt.scatter(df['GrLivArea'], df['SalePrice'])\nplt.plot(df['GrLivArea'], model.predict(X), color='lightgreen')\n\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2022-10-16T16:32:02.562343Z","iopub.execute_input":"2022-10-16T16:32:02.563020Z","iopub.status.idle":"2022-10-16T16:32:02.814647Z","shell.execute_reply.started":"2022-10-16T16:32:02.562986Z","shell.execute_reply":"2022-10-16T16:32:02.813714Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# R Squared evaluation\nmodel.score(X, y)","metadata":{"execution":{"iopub.status.busy":"2022-10-16T16:32:02.815879Z","iopub.execute_input":"2022-10-16T16:32:02.817365Z","iopub.status.idle":"2022-10-16T16:32:02.826848Z","shell.execute_reply.started":"2022-10-16T16:32:02.817314Z","shell.execute_reply":"2022-10-16T16:32:02.825791Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"<a id=\"6\"></a>\n# 🙇 Conclusion","metadata":{}},{"cell_type":"markdown","source":"<div \n     style=\"padding: 20px; \n            color: #3b3b3b;\n            font-size: 115%;\n            text-align: left;\n            display: fill;\n            border-radius: 5px;\n            background-color: #d7ffd7;\n            overflow: hidden;\n            font-weight: 600;\n            border: 5px solid #ffcfa0;\"\n     >\n    With this, our Linear Regression notebook has come to an end. <br>\n    If there are any mistakes, please point them out to me in the commets, this is a living and breathing document.\n</div>","metadata":{}},{"cell_type":"markdown","source":"<div \n     style=\"padding: 20px; \n            color: #3b3b3b;\n            font-size: 115%;\n            text-align: left;\n            display: fill;\n            border-radius: 5px;\n            background-color: #e8f4ff;\n            overflow: hidden;\n            font-weight: 600;\n            border: 5px solid #ffcfa0;\"\n     >\n    Stay tuned for more notebooks in the ML Grandmaster series. <br>\n    I hope you find this series of notebooks useful/fun!\n</div>","metadata":{}},{"cell_type":"markdown","source":"## ML Grandmaster Notebooks\n<br>\n<div\n     style=\"float: left; position: relative; width: 100%;\"\n     >\n    <table\n           style=\"float: left; font-size: 16px\"\n           >\n      <tr>\n        <th><b>📈 Machine Learning Fundamentals</b></th>\n        <th><b>🌳 Decision Trees</b></th>\n      </tr>\n      <tr>\n        <td><a href=\"https://www.kaggle.com/chazzer/ml-grandmaster-linear-regression/\">Linear Regression</a></td>\n        <td><a href=\"https://www.kaggle.com/code/chazzer/ml-grandmaster-decision-tree-classifier\">Decision Tree Classification</a></td>\n      </tr>\n      <tr>\n        <td>Logistic Regression [work in progress]</td>\n        <td><a href=\"https://www.kaggle.com/code/chazzer/ml-grandmaster-decision-tree-regressor\">Decision Tree Regression</a></td>\n      </tr>\n      <tr>\n        <td></td>\n        <td><a href=\"https://www.kaggle.com/code/chazzer/ml-grandmaster-random-forest/\">Random Forest</a></td>\n      </tr>\n      <tr>\n        <td></td>\n        <td><a href=\"https://www.kaggle.com/chazzer/ml-grandmaster-gradient-boosting-xgboost/\">Gradient Boosting (XGBoost)</a></td>\n      </tr>\n    </table>\n</div>","metadata":{}},{"cell_type":"markdown","source":"<div \n     style=\"padding: 20px; \n            color: white;\n            font-size: 250%;\n            text-align: center;\n            display: fill;\n            border-radius: 5px;\n            background-color: #66023C;\n            overflow: hidden;\n            font-weight: 700;\n            border: 5px solid #F28C28;\"\n     >\n    🏆 Machine Learning Grandmaster\n</div>","metadata":{}}]}