diff --git a/Week2/.ipynb_checkpoints/exercise4-checkpoint.ipynb b/Week2/.ipynb_checkpoints/exercise4-checkpoint.ipynb new file mode 100644 index 0000000..067253a --- /dev/null +++ b/Week2/.ipynb_checkpoints/exercise4-checkpoint.ipynb @@ -0,0 +1,516 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Notebook showing data visualisation methods\n", + "#### by Salih MSA" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Importing" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Importing libraries" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import pandas as pd\n", + "\n", + "import statistics # mean, median, etc.\n", + "\n", + "# Data visualisation functionality\n", + "import matplotlib.pyplot as plt\n", + "%matplotlib inline\n", + "import seaborn as sns\n", + "\n", + "from sklearn.model_selection import train_test_split # method to split dataset into 4\n", + "from sklearn.linear_model import LinearRegression # linear regression algorithm\n", + "from sklearn.metrics import mean_squared_error, mean_absolute_error # accuracy testing method" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Importing data" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "# note: below shows manually creating a data frame (ie no external imports) (though technically from a programmers standpoint Python reads from a bloody external file / stream, therefore what possible optimisation is already squandered. but I digress)\n", + "days = ['Mon', 'Tue', 'Wed', 'Thu', 'Fri', 'Sat', 'Sun']\n", + "row_names = [\"max_temp\", \"min_temp\", \"avg_temp\", \"wind\"]\n", + "vals = [ # list of values\n", + " [30,31,32,28,27,29,26], # max_temp\n", + " [23,22,20,24,18,19,10], # min_temp\n", + " [25,28,28,26,23,25,24], # avg_temp\n", + " [50,100,40,65,80,75,50] # wind\n", + "]\n", + " \n", + "data = pd.DataFrame(vals, columns=days, index=row_names) # specifiying column names as each given day" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Data exploration & Preprocessing" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Index: 4 entries, max_temp to wind\n", + "Data columns (total 7 columns):\n", + " # Column Non-Null Count Dtype\n", + "--- ------ -------------- -----\n", + " 0 Mon 4 non-null int64\n", + " 1 Tue 4 non-null int64\n", + " 2 Wed 4 non-null int64\n", + " 3 Thu 4 non-null int64\n", + " 4 Fri 4 non-null int64\n", + " 5 Sat 4 non-null int64\n", + " 6 Sun 4 non-null int64\n", + "dtypes: int64(7)\n", + "memory usage: 256.0+ bytes\n" + ] + } + ], + "source": [ + "data.info()" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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MonTueWedThuFriSatSun
max_temp30313228272926
min_temp23222024181910
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wind501004065807550
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" + ], + "text/plain": [ + " Mon Tue Wed Thu Fri Sat Sun\n", + "max_temp 30 31 32 28 27 29 26\n", + "min_temp 23 22 20 24 18 19 10\n", + "avg_temp 25 28 28 26 23 25 24\n", + "wind 50 100 40 65 80 75 50" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 1) Create scatterplot of wind vs each day" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['Mon', 'Tue', 'Wed', 'Thu', 'Fri', 'Sat', 'Sun'] :: [ 50 100 40 65 80 75 50]\n" + ] + } + ], + "source": [ + "# We want to use the correlation of the an independant variables to determine our model\n", + "y = data.iloc[-1,:].values\n", + "x = days\n", + "print(x, \"::\", y)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Create scatterplot\n", + "plt.title('day x wind')\n", + "plt.xlabel('day')\n", + "plt.ylabel('wind')\n", + "plt.scatter(x, y, alpha=0.5) # ...where alpha is size of points\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 2) Create scatterplot of average temperature vs each day, where each point varies in size based on value's magnitude" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "x = days\n", + "y = data.iloc[-2,:].values # avg_tmp row" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Create scatterplot\n", + "plt.title('day x temperature')\n", + "plt.xlabel('day')\n", + "plt.ylabel('temperature')\n", + "\n", + "def calculatePointSize(values, default_size=250):\n", + " \"\"\"\n", + " @brief Function to calculate size based on points given\n", + " Essentially, a standard size is set, and depending on how a given point deviates from the mean, each points respective size is altered\n", + " This allows for our values, which are quite similar, to be distinguishable on a plot\n", + " @param values - list containing values of points\n", + " @return list of point sizes\n", + " \"\"\"\n", + " mean = statistics.mean(values)\n", + " sizes = [default_size for i in range(0, len(values), 1)]\n", + " for i in range(0, len(values), 1):\n", + " if values[i] < mean:\n", + " sizes[i] -= (mean - values) * 100\n", + " else:\n", + " sizes[i] += (values - mean) * 100\n", + " return sizes[0]\n", + "\n", + "plt.scatter(x, y, s=calculatePointSize(y)) \n", + "# provide additional argument 's(ize)'\n", + "# since all points are very similar, the size of each is with regards to how it varies from the mean\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 3) Create lineplot of average temperature vs each day" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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5Mjkzh/6pjemX2tjrcCLW7Wd2oWNKA+59N4vDxdbB0xioPvF3VtXZx65U1f8CfWv6YFWdo6qiqn0ryjdV9SP3tQmq+s/ahx38/rt+Dxv3FNrZvsfiY6N5dHQftuwr4q+fr/U6HGOCQnWJP7Ga12LrOpBwM2nuJpol1uPCPlbC6bUhHZty7ZB2vDRnE1lbD3gdjjGeqy7xr680NPM/InIBsNF/IYW+TXsK+WpNHtcObmclnEFi4gXdSWlYj3umZVNSZh08TWSrLivdCfxNRCaJyO3uMhlnfP+OgEQXoqbMyyE2WrjOSjiDRlJ8LA9d1ptVO/J5Ybadt5jIVmXid2+s6gN8DaS5y9dAX1W1wdIqHDpayjuLt3Jhn1Y0T4r3OhxTyXm9WnJB75Y89eU6NuYd8jocYzwTU92LqnoUeCVAsYSF6Uu3UmAlnEHrwUt7MXf9Hu6dns3rPxtKVJR4HZIxAWcD0HWoooSzX9tGDGjXxOtwzHE0T4znvot6sGDTPt5cvMXrcIzxhCX+OjRn/R425BUyYXia16GYalyZnsqwjk3540er2JV/xOtwjAm4E0r8ItJERGqs4Y9UkzNzSGkYZyWcQU5E+OPoPhSXlnP/e8u9DseYgKsx8YvILBFJEpFkYCnwoogc24Ih4uXuLeQ/a3Zz7ZD21IuJ9jocU4MOKQ248+yufLpiF58stw6eJrL4csbfyO2qORqYoqpDcHrtmEqmzMslWqyEM5T87NQO9GqdxO/eW8HBohKvwzEmYHxJ/DEi0gpn0pSZfo4nJBUeLeWtRVu4sE8rWlgJZ8iIiY7isTF92VdYzKPWwdNEEF8S/x+AT4ENqrpIRDoCx508JVJN/2YbBUdLbWrFENS7TSN+OqIDbyzaQuYG6+BpIkONiV9V33Ybrd3iPt+oqmP8H1poUHVKOPu2bcQp7Rp7HY6phTvP7kr7pgn8Zno2R0rKvA7HGL/z5eJuVxH5UkSWu8/7ishv/R9aaJi7fi/rdx9i/LA0nCkITKipHxfNo6P6kLP3ME99aV9mTfjzZajnReBeoARAVbNw+ukbYFLmJlIaxnFxPyvhDGUZnVO4Mr0tL8zeyPJtB70Oxxi/8iXxJ6jqwmPW2YwWwOa9h/ly9W6uGdzOSjjDwG8u7EGThDgmTs+i1Dp4mjDmS+LfIyKdAAUQkcsBK3zG6cLplHC29zoUUwcaJ8Tx4CW9WL4tn5fnbvI6HGP8xpfEfyvwPNBdRLbhtGu+xZ9BhYLCo6W8uXgL5/duSctGVsIZLi7s05Jzerbgyc/Xkru30OtwjPELX6p6Nqrq2UAzoLuqjlDVHL9HFuTe/WYbBUdKucH68oQVEeGhS3sTGxXFvdOzUVWvQzKmzvlS1dNCRF4C3lHVAhHpKSI/CUBsQauihLN3myROsS6cYadlo3juuaA7mRv28vaSrV6HY0yd82WoZxLODVyt3edrcYZ7Ilbmhr2s232ICRkdrIQzTF07uB2D05J55MNV7C6wDp4mvPiS+FNU9S2gHEBVS4GIvstlUmYOyQ3iuLivlXCGq6go4dExfSgqLuPBD1Z6HY4xdcqXxF8oIk35rqpnKBCxhc5b9h3mi1W7uHZwO+JjrYQznHVq1pBfnNWZD7N28PnKXV6HY0yd8SXx/wp4H+gkInOBKcDtfo0qiP17fi5RIlw31LpwRoIbf9SJ7i0T+d2M5eQfsQ6eJjxUm/hFJBo4zV0ygJuAXu7duxHncHEpbyzczPm9W9KqUX2vwzEBEBcTxZ/G9GV3wREe/2S11+EYUyeqTfyqWgZco6qlqrpCVZerasSe9sz4Zjv5R2wi9UjTP7UxNwzvwKvzN7MoZ5/X4Rhz0nwZ6pkrIs+KyKkickrF4vfIgoyqMilzE71aJ5He3ko4I81d53albZP6TJyWZR08TcjzJfH3B3rh9OX/i7s84ceYgtK8jXtZu+sQ4zOsC2ckSoiL4ZFRfdiQV8jfv1rvdTjGnJSYmt6gqmcEIpBgN2muU8J5Sb/WNb/ZhKXTujZj9IA2/GPWBi7q24ruLZO8DsmYWqkx8YvIr46z+iCwRFWX1XlEQaiihPPm0zpZCWeE++3FPZm1No97pmUz/ZYMoqPs258JPb4M9aQDNwNt3OUm4HzgRRG5u6qNRCRVRL4SkZUiskJE7qj02u0istpd//hJHoPfvTo/FxHh+qHWhTPSJTeI44GRPfl2ywEmZ+Z4HY4xtVLjGT/QFjhFVQ8BiMgDwIfAj4AlQFWJuxS4S1WXikgisEREPgdaAJcC/VT1qIg0P9mD8Kei4jLeWLSF83q1oHVjK+E0cEm/1sz4Zht//nQN5/RsQWpygtchGXNCfDnjbw4crfS8BGihqkXHrP8eVd2hqkvdxwXAKpxvDLcAf1LVo+5ru2sZe0DMWLaNg0UlTMjo4HUoJkiICA+P6oMI3DdjuXXwNCHHl8Q/FVggIg+4Z/tzgddEpAHgUxMTEUkDBgALgK7AqSKyQES+FpFBVWxzo4gsFpHFeXl5vuymzlV04ezRKolBaVbCab7TpnF97jq3G7PX5rFgk9X2m9DiSz/+h4AbgQPucrOq/kFVC1X1upq2F5GGwDTgTlXNxxleSgaGAr8G3pLj1Eeq6guqmq6q6c2aNTuBQ6o78zfuY/XOAm6wEk5zHNcObkfjhFgb6zchx5czfoB4IF9VnwJyRcSncQ8RicVJ+lNVdbq7eiswXR0Lcbp+ppxg3AExOTOHJgmxXNLfSjjND9WPi+aqQal8tnIX2w4UeR2OMT7zZSKWB4B7gHvdVbHAqz5sJ8BLwCpVfbLSSzOAM9z3dAXigD0nFHUAbN1/mM9W7uRq68JpqjF2aHtUlVfn53odijE+8+WMfxRwCVAIoKrbgUQfthsOjAXOFJFl7nIh8DLQUUSWA28A4zUIr469On8zgJVwmmq1bZLAOT1b8MbCzdbKwYQMX8o5i1VVRaSiH38DXz5YVecAVQ2MX+9jfJ44UlLGG4s2c16vlrSxEk5TgwkZHfh0xS7eX7adKweleh2OMTXy5Yz/LRF5HmgsIj8DvgBe9G9Y3npv2TYOHC5hvHXhND4Y2jGZbi0SmZSZY6WdJiT4UtXzBPAOzkXabsD9qvqMvwPziqryytwcurdMZEiHZK/DMSFARJgwPI2VO/JZlLPf63CMqZFPVT2q+rmq/lpV/09VP/d3UF5auMkp4ZxgJZzmBFzWvw2N6ltppwkNVSZ+ESkQkfyqlkAGGUiTMnNonBDLpf3beB2KCSH146K5elAqn6zYyXYr7TRBrsrEr6qJqpoEPAVMxGm30BantPNvAYkuwLYdKOKzlbu4alAq9eOshNOcmOvd0s6pC6y00wQ3X4Z6LlHV51S1QFXzVfUfOE3Wws6r83NRVcZaCaephdTkBM7u0YLXF26x0k4T1HxJ/IUicp2IRItIlIhch1vTH06OlJTxxsLNnNOzBW2bWLdFUzsTMtLYV1jMB99u9zoUY6rkS+K/FrgS2OUuV7jrwsr7y7az/7B14TQnZ1inpnRt0dBKO01Q86WcM0dVL1XVFFVtpqqXqWpOAGILGGci9Ry6tUhkaEcr4TS1JyKMz0hjxfZ8luRaaacJTtVV9fxWRKrMgiJypohc7J+wAmtRzn5W7shnwnAr4TQnb9SANiTFx/CKlXaaIFVdy4Zs4AMROQIsBfJwunR2Afrj3MH7R38HGAiTM3NoVD+Wy6yE09SBhLgYrhqUystzc9hxsIhWjazthwku1ZVzvqeqw3Hm210BRAP5OJ05B6vqL1XVmxlS6tD2A0V8smInV1sJp6lD44alUa7KVLfZnzHBpMYmbaq6DlgXgFg8MXWBU8JpXThNXUpNTuCs7i14feFmbjuzs7X2NkHF14lYwtKRkjJeX7iFs3vYhNmm7t0wPI29hcXMzNrhdSjGfE9EJ/4Pvt3OvsJiJlgXTuMHGZ2a0qV5QyZbaacJMhGb+CtKOLu2aMiwTk29DseEoYrSzuxtB1m62Uo7TfDwZerFriLypTtjFiLSV0R+6//Q/GtJ7n5WbM9nvHXhNH40akAbEuNjmJRp/XtM8PDljP9FnPl2SwBUNQu42p9BBcIrmTkkxccwaoCVcBr/aVAvhqvSU/k4ewe78o94HY4xgG+JP0FVFx6zrtQfwQTKjoNFfLJ8J1cNSiUhzpfZJ42pvXHD0ihTZapNyG6ChC+Jf4+IdAIq5ty9HAjpMoWp8zdTrsq4YWleh2IiQLumCZzVvTmvLdzM0VLr2mm850vivxV4HuguItuAO3Fu6gpJTgnnZs7qbiWcJnDGZ6Sx51AxH1pppwkC1SZ+EYkGfq6qZwPNgO6qOkJVQ/Y768ysHey1Ek4TYCM6p9C5uXXtNMGh2sSvqmXACPdxoaoWBCQqP1FVJmfm0Ll5Q4Z3thJOEzgiwvhh7cnaepBvthzwOhwT4XwZ6vlGRN4XkbEiMrpi8XtkfrB0836ytx20Ek7jidGntCWxXgyT5uZ4HYqJcL4k/nhgL3AmMNJdQrId86TMXBLjYxhtJZzGAw3qxXBFeiofWWmn8ZgvTdpuCEQg/rYr/wgfZ+9gfEYaDepZCafxxrhh7XklcxNTF2zmV+d09TocE6FqzIAi8gpuKWdlqvpjv0TkJ1Pn51Kmyrhh1oXTeCctpQFndGvOaws2c+sZnagXY107TeD5MtQzE/jQXb4EkoBD/gyqrh0tLeO1hZs5s1tz2jdt4HU4JsJNyEhjz6GjfJRtpZ3GG74M9Uyr/FxEXgfm+C0iP/gwawd7DhUzYXia16EYw4jOKXRs1oBJmbmMGtDW63BMBKpNd84uQPO6DsRfKrpwdmrWgBGdU7wOxxiiooQJGWl8u+UA31jXTuMBX7pzFohIfsUCfADc4//Q6sY3Ww6QtfUgE6yE0wSR0ae0pWG9GCbbhOzGAzUmflVNVNWkSkvXY4d/jkdEUkXkKxFZKSIrROQOd/3vRWSbiCxzlwvr4kCqMjkzh8R6MYw+xb5Sm+DRsF4MV6S35cPsHewusNJOE1i+nPF/6cu64ygF7lLVnsBQ4FYR6em+9ldV7e8uH51QxCdgd/4RPszawRXpqVbCaYLOuGFplJQpry2wCdlNYFWZ+EUkXkSSgRQRaSIiye6SBtR4B5Sq7lDVpe7jAmCVL9vVpakLNlsJpwlaHVIacHq3ZkxdsJni0nKvwwkLR0rKGP/yQh75cKX9TKtR3Rn/TcASoLv734rlPeDZE9mJ+8diALDAXXWbiGSJyMsi0qSKbW4UkcUisjgvL+9Edvc/3Vom8pPhHUhLsRJOE5wmZKSRV3CUj5dbaWddeO6r9Xy9No8X/7uJa1+cb3dIV0Fq6hQoIrer6jO13oFIQ+Br4BFVnS4iLYA9ODeFPQS0qulmsPT0dF28eHFtQzAmaJWXK2c9+TWN6scy49bhXocT0lbvzOfip+cwsl9rTu/WjInTsmlQL4Znrx3A0I6R2ZRRRJaoavqx6325uPuMiPQWkStFZFzF4uNOY4FpwFRVne5+3i5VLVPVcpxpHQef2KEYEz6iopyuncu2HGCZde2stbJy5Z5p2STVj+V3F/fk0v5teO+24STFx3Ddvxbw4uyN1g67El8u7j4APOMuZwCPA5f4sJ0ALwGrVPXJSutbVXrbKGD5CcZsTFgZM7AtDeKirbTzJEzOzOHbLQd4YGRPkhvEAdC1RSLv3Tacs3s055GPVnHra0s5dDSkZ42tM77cwHU5cBaw023Y1g9o5MN2w4GxwJnHlG4+LiLZIpKF84fkl7WM3ZiwkBgfyxXpqczM2m6lnbWwZd9hnvhsDad3a8Yl/Vp/77XE+Fj+ef1A7r2gO58s38mlz85h/e6QnlakTviS+IvcYZlSEUkCdgOpNW2kqnNUVVS1b+XSTVUdq6p93PWXqKpd1TIRb9yw9pSUKa8v2OJ1KCFFVblvhjNo8PBlvY97k6aIcNNpnXj1J0M4cLiES5+dG/FTYPqS+BeLSGOc8fglwFJgnj+DMibSdGzWkNO6NmPqglwrQzwBM5ZtY/baPO4+rxttm1Q/h3ZG5xRm/mIEXVsmcutrS3l45kpKyiLzZ13TnLsCPKqqB1T1n8A5wPhw6dFvTDCZkJHGbivt9NneQ0f5wwcrGdCuMWOHpfm0TatG9XnzxmGMG9aef83ZxHX/WhCRw2s1zbmrwEeVnueoapbfozImAp3WtRlpTRPsIq+PHpq5kkNHS3lsTF+io3zvwxUXE8UfLu3NX6/qR9bWA1z89BwW5ezzY6TBx5ehnqUiMsjvkRgT4aKihHHD0li6+QBZWw94HU5Q+2rNbmYs284tp3ema4vEWn3GqAFteffnw0mIi+aaF+bz8pxNEVPy6UviHwLME5EN7t22FRU5xpg6dnm6U9o5yc76q3ToaCn3Tc+mc/OG3HpGp5P6rB6tknjvthGc3q05f5i5kl+8sYzCCCj59CXxnwd04rvJ1i92/2uMqWNJ8bGMGdiWmd/uYM+ho16HE5Se+HQNO/KP8NiYPnUydWWj+rG8MHYgvz6vGx9mbeeyv89lQ15ITTJ4wny5czcXp3zzTPfxYV+2M8bUzrhhaRSXlfO6de38gaWb9zN5Xg5jh7ZnYPvkOvvcqCjh1jM6M+XHQ9hbWMylz87lkzC+yO7rnbv3APe6q2KBV/0ZlDGRrHPzhpzaJYVXF+RGbLnh8RSXljNxWhYtk+L59Xnd/LKPEV1S+OD2EXRq1oCbX13Kox+vojQMfwe+nLmPwmnRUAigqtuB2l1NMcb45IbhaezKP8ony3d6HUrQ+MesDazddYiHL+tNYnys3/bTpnF93rp5GNcNacfzX29k7EsLySsIr2E3XxJ/sVvWqQAiYj2OjfGz07s2p33TBLvI61q3q4Bnv1rHyH6tOatHC7/vr15MNI+M6sMTV/Rj6eb9jHxmDktyw2d+ZF8S/1si8jzQWER+BnyBcxevMcZPKko7l+TuJ3vrQa/D8VR5uTJxutNi+YGRPWveoA5dPrAt03+eQVxMFFe/MI/JmTlhUfLpy8XdJ4B3cNordwXuP5n+/MYY31yR3pYEK+1k6oJcluTu57cX9SSlYb2A779X60Z8cNsITu3SjAfeX8Ev31zG4eLQLvn0tTonG/gvMNt9bIzxs6T4WMac0pYPvt0esaWd2w8U8aePV3NqlxTGnBLQmVu/p1FCLP8al85d53TlvW+3M/q5TDbtKfQsnpPlS1XPT4GFwGicFs3zRaTaGbOMMXVjfEZ7isvKeWNh5JV2qiq/m7GccoU/jupz3M6bgRQVJdx+Vhcm3TCYnflHuOSZOXy2IjQvvvtyxv9rYICqTlDV8cBAnPJOY4yfdW6e6JR2zt8ccaWdM7N28OXq3dx1bldSk6vvvBlIp3Vtxge3jSAtpQE3/nsJj3+ymrLy0Br39yXx7wUqz1xQ4K4zxgTA+GFp7Mw/wqchenZZG/sLi/n9+yvo27YREzLSvA7nB1KTE3j75mFcMziV52ZtYPzLC9kbQsNxviT+9cACEfm9ezPXfGCtiPxKRH7l3/CMMWd0b0675Mjq2vnwh6s4WFTCn0b3JSY6OBsFxMdG8+jovjw+pi8Lc/Yx8pk5ITNvsi8/0Q3ADNw6fuA9YBPOTVx2I5cxfhYdJYwb1p5FOftZvi38Szv/uy6PaUu3ctNpHenZOsnrcGp05aBUpt+SQVSUcOU/5/Hq/NygL/mUYA8QID09XRcvXux1GMZ45mBRCUP/+CUX923Fn6/o53U4fnO4uJTz/jab2KgoPrrjVOJjT74JW6AcOFzMHW8s4+u1eYw5pS0PX9ab+nHexi8iS1Q1/dj1vlT1pIvIuyKy1G3LnGVtmY0JrEb1Yxl9Shve+3Z7SI0ln6i/fr6WLfuKeHR0n5BK+gCNE+J4ZcIg7jirC9O/2crof2SSuzc4Sz59GeqZCrwCjMFpx1yxGGMCaEJGGsWl5byxKDwnZM/aeoCX5mzi2iHtGNKxqdfh1EpUlPDLc7ry8vhBbD9QxMhn5vDlql1eh/UDviT+PFV9X1U3qWpuxeL3yIwx39OlRSLDOzfl1fm5YdcxsqSsnLvfySKlYT0mXtDd63BO2hndmzPz9hGkJifwk8mLefKzNUFV8ulL4n9ARP4lIteIyOiKxe+RGWN+YEJGB3YcPMJnK4PvLPJkvDB7I6t3FvDQZb1J8mPnzUBKTU5g2i0ZXDGwLU//Zz0TXlnI/sJir8MCfEv8NwD9gfP5bpjnYj/GZIypwpndm9O2SX0mzc3xOpQ6szHvEE99uY4LerfkvF4tvQ6nTsXHRvP45X15dHQfFmzcx8XPzAmK+ZR9SfyDVDVdVcer6g3uYi0bjPFAdJQwflgaC3P2sWJ76Jd2lpcr907PJj4migcv7eV1OH4hIlwzuB1v3zwMgMv/Mc/zFhy+JP5MEQlsL1RjTJWuTE+lfmx0WNzQ9caiLSzYtI/7LupB88R4r8Pxq36pjfng9hEM6ZjMxOnZ3P3OtxwpKfMkFl8S/1BgmYiscUs5s62c0xjvNEqIZdQpbXhv2Xb2BcmYcW3syj/Cox+tYljHplyZnup1OAGR3CCOSTcM5vYzO/PW4q1c/s9Mtuw7HPA4fEn85wNdgHP5bnzfyjmN8dD4YWkcLS3njUWh27Xz/veWU1xWzqOjve+8GUjRUcJd53bjX+PSyd17mJHPzmHWmt0BjcGXiVhygVTgTPfxYV+2M8b4T7eWiWR0asqr80KztPOT5Tv4dMUufnlOV9JSInM217N7tuCD20bQMimeGyYt4qkv1lEeoJJPX+7cfQCnDfO97qpY4FV/BmWMqdn4jDS2HzzC5yFW2nmwqITfvbeCXq2T+OmIDl6H46m0lAa8+/PhjOrfhr9+sZafTF7EgcP+H77z5cx9FHAJUAigqtvxoTmbiKSKyFcislJEVojIHce8fpeIqIik1CZwYyLd2T1a0KZx/ZCbmvHRj1axr7CYx8YEb+fNQKofF81fruzHQ5f1Zs76PYx8do7fm/H58lMvVqeTmwKIiK/fy0qBu1S1J84F4lsrqoNEJBXnmkHoDlAa47GKrp0LNu1j1Y58r8PxSeaGPbyxaAs/HdGB3m0aeR1O0BARxg5tz1s3DaO0TBnzj0zeWuy/1hy+JP63ROR5oLGI/Az4AvhXTRup6g5VXeo+LgBWARWTZv4VuJvvWj0bY2rhqkGpxMdGhURp55GSMn4zPZv2TRO48+yuXocTlAa0a8LM20cwsH0T7n4ni3unZ3O0tO5LPn25uPsE8A4wDegG3K+qT5/ITkQkDRiAM6HLpcA2Vf22hm1uFJHFIrI4Ly/vRHZnTMRonBDHqAFtePebbUHTDqAqT325jpy9h3l0VB/P2xUHs6YN6zHlx4O55fROvL5wM1+uqvuKH18u7j6mqp+r6q9V9f9U9XMReczXHYhIQ5w/GnfiDP/8Bri/pu1U9QX3juH0Zs2a+bo7YyLO+AyntPNNPw4NnKzl2w7ywuyNXJnelozOdlmvJjHRUdxzfndm3j6CC/u0qvPP92Wo55zjrLvAlw8XkVicpD9VVacDnYAOwLcikgO0BZaKSHg16DAmgLq3TGJox2T+HaSlnaVl5UycnkWThDjuu9CaAJwIf10HqTLxi8gtIpINdKs8AYuIbAJqvHNXnDsyXgJWqeqTAKqararNVTVNVdOArcApqho5s0gb4wcTMjqw7UARX/hhWOBkvTx3E8u35fOHS3vRKCE8Om+GuurO+F/DuUP3fb4/ActAVb3eh88eDowFzhSRZe5y4ckGbIz5obN7NHdLOzd5Hcr35O4t5MnP13JOzxZc0Nu+2AeLmKpeUNWDwEHgmtp8sKrOAaq9D9s96zfGnKSY6CjGDmvPnz5ezeqd+XRv6f0k5apO583YqCgeurR3RLVlCHZ294QxYeKq9FTqxQRPaefbS7aSuWEv91zQnZaNwrvzZqixxG9MmGjS4LvSzkDc9l+d3QVHeOTDVQxOS+bawe08jcX8kCV+Y8LI+Iw0jpSU86bHE7I/+MFKiorLeHRMH6KibIgn2FjiNyaM9GiVxJAOyUyZl+vZ5N6fr9zFh1k7+MVZnenUrKEnMZjqWeI3JsxMyEhzSzsD37Uz/0gJv5uxnO4tE7nxR50Cvn/jG0v8xoSZc3q2oHWjeE8u8j7+yWp2FxzhT2P6Ehdj6SVY2W/GmDATEx3F9cPak7lhL2t2FgRsv4ty9vHq/M3cMLwD/VMbB2y/5sRZ4jcmDF09qJ1T2jkvJyD7O1JSxsRpWbRtUp+7zrXOm8HOEr8xYSi5QRyX9m/Nu0u3cfBwid/39/ev1rMhr5A/jupDQlyV94WaIGGJ35gwNT4jjaKSMr9O6AGwemc+/5i1gdGntOFHXa2TbiiwxG9MmOrVuhGD05KZPC/Hb6WdZeXKPdOyaVQ/lt9dZJ03Q4UlfmPC2IThaWzdX8R/Vvuna+fkzBy+3XKA+0f2pEmDOL/sw9Q9S/zGhLFze7agVaN4v3Tt3LLvMH/+dA1ndGvGJf1a1/nnG/+xxG9MGIuJjuL6oe2Zu34v63bVXWmnqnLfjOVECTw8qo913gwxlviNCXPXDG5HXB2Xds5Yto3Za/O4+/zutGlcv84+1wSGJX5jwlxygzgu7deaaUu2cbDo5Es79x46yh8+WMkp7Rpz/dD2dRChCTRL/MZEgIrSzrfroLTzoZkrOXS0lD+N6Uu0dd4MSZb4jYkAvds0YlBak5Pu2vnVmt3MWLadW8/oTNcWiXUYoQkkS/zGRIjxGWls3neYWWtqV9p56Ggp903PpkvzhtxyunXeDGWW+I2JEOf1aknLpHgm1bJr5xOfrmFHvtN5s15MdN0GZwLKEr8xESI2Oorrh7bjv+v2sH73iZV2Lt28n8nzchg3tD0D2zfxU4QmUCzxGxNBrh7cjrjoKCZn5vq8TXFpOROnZdEqKZ5fn9/dj9GZQLHEb0wESWlYj5H9WjNt6Vbyj/hW2vmPWRtYu+sQD4/qTcN61nkzHFjiNybCTMhI43BxGW8v3lrje9ftKuDZr9ZxSb/WnNm9RQCiM4Fgid+YCNOnbSMGtm/ClHk5lFdT2llerkycnk2DejHcP9I6b4YTS/zGRKDxGWnk7j3MrLVVl3a+uiCXJbn7uf/inqQ0rBfA6Iy/WeI3JgJd0LslLZLqMamKi7zbDxTx2MerObVLCqMGtAlwdMbfLPEbE4Fio6O4bkh7Zq/NY0Peoe+9pqr8bsZyyhX+aJ03w5IlfmMi1DVuaeeUY27ompm1gy9X7+auc7uSmpzgTXDGryzxGxOhmiXW4+K+rXhnyVYK3NLO/YXF/P79FfRr24gbhnfwOELjL5b4jYlg4zPSKCwu450lTmnnwx+u4mBRiXXeDHN+S/wikioiX4nIShFZISJ3uOsfEpEsEVkmIp+JiM3ZZoxH+qU2ZkC7xkzOzOHrtXlMW7qVm0/rRI9WSV6HZvzIn2f8pcBdqtoTGArcKiI9gT+ral9V7Q/MBO73YwzGmBpMyEgjZ+9hbp26lI7NGnDbmZ29Dsn4md8Sv6ruUNWl7uMCYBXQRlXzK72tAVD75uDGmJN2Qe9WNEus50yuMrov8bHWeTPcBaTxhoikAQOABe7zR4BxwEHgjCq2uRG4EaBdu3aBCNOYiBQXE8Ujl/VmV/4RBndI9jocEwCi6t8TbhFpCHwNPKKq04957V4gXlUfqO4z0tPTdfHixX6M0hhjwo+ILFHV9GPX+7WqR0RigWnA1GOTvmsqMMafMRhjjPk+f1b1CPASsEpVn6y0vkult10KrPZXDMYYY37In2P8w4GxQLaILHPX/Qb4iYh0A8qBXOBmP8ZgjDHmGH5L/Ko6BzjeHSAf+WufxhhjamZ37hpjTISxxG+MMRHGEr8xxkQYS/zGGBNh/H4DV10QkTycCqDaSAH21GE4XrJjCT7hchxgxxKsTuZY2qtqs2NXhkTiPxkisvh4d66FIjuW4BMuxwF2LMHKH8diQz3GGBNhLPEbY0yEiYTE/4LXAdQhO5bgEy7HAXYswarOjyXsx/iNMcZ8XySc8RtjjKnEEr8xxkSYkE78IqIi8mql5zEikiciM72MqzZEpKk7Af0yEdkpItsqPY/zOj5fiMhfReTOSs8/FZF/VXr+FxH5lQ+fkyYiy/0Ups+q+Z0cEJGVXsd3MkSkrNKxLXNnyTv2PR+JSOPAR+cbEblPRFaISJZ7DEOqee8EEWkdyPh8dSLHUVcCMvWiHxUCvUWkvqoWAecA2zyOqVZUdS/QH0BEfg8cUtUnvIypFuYCVwJ/E5EonBtPkiq9ngH80ovAaqOq34mbJEPu5OIYRara/3gvuHNpiKpeGNiQfCciw4CLgVNU9aiIpADVnSBNAJYD2wMQns9qcRx1IqTP+F0fARe5j68BXq94QUSSRWSG+5d0voj0ddf/XkReFpFZIrJRRH7hQdw1EpFJInJ5peeHKj3+tYgsco/tQW8i/IFMYJj7uBfOP7QCEWkiIvWAHoCKyNcissT9RtAKQEQGisi3IvItcKsn0Z+YaBF50T1T+0xE6gO4/0+lu49TRCTH0yh95H7LWiMiU3B+b6kikuMmomDUCtijqkcBVHWPqm4XkfvdfxfLReQFcVwOpANT3TPq+p5G/n1VHcf/fvYiki4is9zHdZK7wiHxvwFcLSLxQF/cCd1dDwLfqGpfnElgplR6rTtwHjAYeECcaSJDgoicC3TBib0/MFBEfuRpUICqbgdKRaQdztn9PJzfxzCcf3irgL8Cl6vqQOBl4BF381eA21W1X8ADr50uwN9VtRdwgNCbQrR+pWGed911XYDnVLWXqta2RUqgfIbzx2mtiDwnIqe5659V1UGq2huoD1ysqu8Ai4HrVLW/OzoQLKo6juqcdO4K9aEeVDXL/ep9DT+c5GUE7j9IVf2PO2ZbMfTwoftX9qiI7AZaAFsDFPbJOtddvnGfN8T5Rzvbs4i+k4mT9DOAJ4E27uODOMNw5wKfO6MJRAM73HHkxqpaEf+/gQsCG/YJ26Sqy9zHS4A070Kple8N9bj/hnJVdb5nEZ0AVT0kIgOBU4EzgDdFZCLON8y7gQQgGVgBfOBdpNWr5jiqc9K5K+QTv+t94AngdKCpj9scrfS4jOD8WZTifitzx8wrxv4EeFRVn/cqsGrMxUn0fXCGDLYAdwH5wCygjaoOq7xBMF9ArMax//9UDB/873cGxAc0opNX6HUAJ0JVy3D+n5olItnATTjf+tNVdYt7XSbofwfHOY7xVP//0UnnrnAY6gFnyOBBVc0+Zv1/gesAROR0nLG0/MCGdlJygIHu40uAiq90nwI/FpGGACLSRkSaBz6848rEuVi1T1XLVHUf0BhnuOd1oJl7QQsRiRWRXqp6ADggIiPcz7gu8GHXmRy++51dXs37zEkQkW4i0qXSqv7AGvfxHvffRuWffwGQGKDwfFbFceTy/f+P6nwYMRjPck+Yqm4Fnj7OS78HXhaRLOAwzl/SUPIi8J57wfMT3DMyVf1MRHoA89whk0PA9cBurwKtJBunmue1Y9Y1VNXd7oW2p0WkEc7/f3/D+Tp+A87vSnHGPUPVE8BbInIj8KHXwYSxhsAz7rfFUmA9cCPO9ZblwE5gUaX3TwL+KSJFwLAgGuev6jh6AC+JyEM43wbqlLVsMMaYCBMuQz3GGGN8ZInfGGMijCV+Y4yJMJb4jTEmwljiN8aYCGOJ35gT4PZK+T+v4zDmZFjiN8aYCGOJ35gaiNMvfa2IzAG6uet+5naB/FZEpolIgogkisimiqZZIpJU+bkxwcISvzHVcBtoXY1zK/2FwCD3peluF8h+OF1Hf6KqBTh3WVa0Cb/afV9JQIM2pgaW+I2p3qnAu6p62O3z9L67vreI/NdtqnUdzvwDAP/CaT+B+99XAhqtMT6wxG9M7UwCblPVPjjzPsQDqOpcIM1tChitqp5PIWnMsSzxG1O92cBlIlJfRBKBke76RJy5BGL5YTfRKThN6uxs3wQla9JmTA1E5D6czq67gc3AUpxOqXcDeTizjCWq6gT3/S2BTUArt+W0MUHFEr8xdcxtPX2pqo71OhZjjics+vEbEyxE5BmcaSMv9DoWY6piZ/zGGBNh7OKuMcZEGEv8xhgTYSzxG2NMhLHEb4wxEcYSvzHGRJj/ByFnnFAOH3JgAAAAAElFTkSuQmCC\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# same x&y as Q2\n", + "# Create lineplot\n", + "plt.title('day x temperature')\n", + "plt.xlabel('day')\n", + "plt.ylabel('temperature (degrees C)')\n", + "plt.plot(x, y)\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 4) Create lineplot of average temperature vs each day, where a) every point is pinpointed on graph, b) area under line is highlighted" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "3" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# same x&y as Q3\n", + "# Create lineplot\n", + "plt.title('day x temperature')\n", + "plt.xlabel('day')\n", + "plt.ylabel('temperature (degrees C)')\n", + "plt.plot(x, y, marker=\"*\", linestyle=\"dashed\")\n", + "plt.fill_between(x, # range of x \n", + " y, # pointSSSS of y (to fill from...)\n", + " min(y)-1, # bottom point of y (...to fill until)\n", + " color='blue', # The outline color\n", + " alpha=0.2) # Transparency of the fill\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 4) Create lineplot of max, average & min temperatures of each day" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "x = days\n", + "y1 = data.iloc[-2,:].values # avg_tmp row\n", + "y2 = data.iloc[-3,:].values # min_tmp row\n", + "y3 = data.iloc[-4,:].values # max_tmp row" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Create lineplot\n", + "plt.title('day x temperature')\n", + "plt.xlabel('day')\n", + "plt.ylabel('temperature (degrees C)')\n", + "plt.plot(x, y1)\n", + "plt.plot(x, y2)\n", + "plt.plot(x, y3)\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 5) Create lineplot of max, average & min temperatures of each day (as Q4), with proper legend and other labelling" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Create lineplot\n", + "plt.title('day x temperature')\n", + "plt.xlabel('day')\n", + "plt.ylabel('temperature (degrees C)')\n", + "plt.plot(x, y1, label=\"avg. temp\")\n", + "plt.plot(x, y2, label=\"min. temp\")\n", + "plt.plot(x, y3, label=\"max. temp\")\n", + "plt.legend()\n", + "plt.show()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.10" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/Week3/Week3.md b/Week3/Week3.md new file mode 100644 index 0000000..aefefd3 --- /dev/null +++ b/Week3/Week3.md @@ -0,0 +1,25 @@ +# 6006CEM_notes +## Week 3 - Logistic Regression (classification) + +### Basic logistic regression + +* Despite its name, it is NOT a regression algorithm, rather a classification algorithm + * ie regression algorithms *return continuous variables* (ie numbers of any range), whilst classification algorithms *return discrete (ie fixed) values* + +* It draws a line which *seperates* values into distinct groups, such that the values fall either side of a drawn line + +* Accuracy of the model is determined by a form of error / loss calculation - in essence it's how many points are correctly classified + * note that it is more complicatd in nature than the one used for Lin.R. + +* when generating a model, we start a random line and move it by (specifiable) number of small increments for a (specifiable) large number of times, until a model with high(est) accuracy is produced + +* note: many classification algorithms only accept numeric labels, therefore conversion (a type of preprocessing) is required beforehand) + +### Multi-class classification + +* essentially, you create multiple models and you see which class identified with the highest overall probability + +* there are two methods of / approaches to classification when the output is multiple dependant variables: + * one-vs-one - a model is produced using the data of two classes at a time (eg. 3 colours: model for red vs green, model for green vs blue, model for blue vs red). generates (k-(k-1))/2 models. approach less prone to an imbalanced / undersampled dataset + * one-vs-rest - models are generated involving the whole dataset, where one target class is positive and the rest are negative (eg. 3 colours: model for red vs blue&green, model for blue vs red&green, model for green vs red&blue). generates k models. approach has less classifiers so trying to find most likely is quicker +