mirror of
https://github.com/mudabbir-ahmad/UNI-PROG3-CW2-MLWP.git
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101 lines
2.4 KiB
Plaintext
101 lines
2.4 KiB
Plaintext
{
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"cells": [
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{
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"metadata": {
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"collapsed": true
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},
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"cell_type": "markdown",
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"source": [
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"## This is the Q2 Notebook!\n",
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"\n",
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"It's tracked via GitHub! hence the need for this line for the init commit"
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],
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"id": "bb3519b1aa083259"
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},
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{
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"metadata": {},
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"cell_type": "markdown",
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"source": [
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"## Part A\n",
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"\n",
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"Q: A. Using (Rating + Reviews + Content Rating + Size in Bytes +\n",
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"Installs_Num), using Logistic regression and KNN, find and discuss the best\n",
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"classification model to predict “Category” (use the training/validation/test\n",
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"partition without cross-validation). **[8 marks]**\n"
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],
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"id": "1baa7daa49445720"
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},
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{
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"metadata": {},
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"cell_type": "code",
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"outputs": [],
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"execution_count": null,
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"source": "",
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"id": "f6fd7137bf91f31e"
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},
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{
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"metadata": {},
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"cell_type": "markdown",
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"source": [
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"## Part B\n",
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"\n",
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"Q: Using (Rating + Reviews + Category + Size in Bytes + Installs_Num),\n",
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"using Logistic regression and KNN, find and discuss the best classification model\n",
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"to predict “Content Rating” (use the training/validation/test partition without\n",
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"cross-validation). **[7 marks]**"
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],
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"id": "f15aa6cca58826fc"
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},
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{
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"metadata": {},
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"cell_type": "code",
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"outputs": [],
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"execution_count": null,
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"source": "",
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"id": "65e55b70ad44b2dc"
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},
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{
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"metadata": {},
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"cell_type": "markdown",
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"source": [
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"## Part C\n",
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"\n",
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"Q: By considering Installs_Num as categorical feature and using (Rating +\n",
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"Reviews + Category + Size in Bytes + Content Rating), find and discuss the\n",
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"best classification model to predict “Installs_Num” (using Logistic regression and\n",
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"KNN) (use the training/test partition without cross-validation) **[8 marks]**"
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],
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"id": "86c040f5e043a39c"
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},
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{
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"metadata": {},
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"cell_type": "code",
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"outputs": [],
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"execution_count": null,
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"source": "",
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"id": "f3535461938f94f5"
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 2
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython2",
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"version": "2.7.6"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}
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