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projects/finding_donors/finding_donors.ipynb

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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": false
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},
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"metadata": {},
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"outputs": [],
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"source": [
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"# Import libraries necessary for this project\n",
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": false
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"metadata": {},
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"outputs": [],
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"source": [
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"# TODO: Total number of records\n",
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": false
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"metadata": {},
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"outputs": [],
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"source": [
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"# Split the data into features and target label\n",
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": false
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"metadata": {},
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"outputs": [],
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"source": [
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"# Log-transform the skewed features\n",
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": false
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"metadata": {},
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"outputs": [],
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"source": [
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"# Import sklearn.preprocessing.StandardScaler\n",
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": false,
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"scrolled": true
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"outputs": [],
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": false
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"metadata": {},
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"outputs": [],
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"source": [
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"# Import train_test_split\n",
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": false
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"metadata": {},
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"outputs": [],
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"source": [
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"# TODO: Calculate accuracy\n",
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": false
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"metadata": {},
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"outputs": [],
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"source": [
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"# TODO: Import two metrics from sklearn - fbeta_score and accuracy_score\n",
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": false
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"metadata": {},
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"outputs": [],
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"source": [
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"# TODO: Import the three supervised learning models from sklearn\n",
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": false
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"metadata": {},
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"outputs": [],
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"# TODO: Import 'GridSearchCV', 'make_scorer', and any other necessary libraries\n",
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": false
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"metadata": {},
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"outputs": [],
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"source": [
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"# TODO: Import a supervised learning model that has 'feature_importances_'\n",
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": false
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"metadata": {},
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"outputs": [],
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"source": [
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"# Import functionality for cloning a model\n",
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 2",
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"display_name": "Python 3",
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"language": "python",
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"name": "python2"
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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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"version": 3
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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": "2.7.12"
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"pygments_lexer": "ipython3",
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"version": "3.6.0"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 0
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"nbformat_minor": 1
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This project has been written in Python 3.x.

projects/practice_projects/naive_bayes_tutorial/Naive_Bayes_tutorial.ipynb

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},
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {},
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{
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"4 0 Nah I don't think he goes to usf, he lives aro..."
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]
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"execution_count": 2,
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"metadata": {},
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"output_type": "execute_result"
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}
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"cell_type": "code",
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"execution_count": 3,
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"outputs": [
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{
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"cell_type": "code",
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"execution_count": 5,
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"execution_count": 4,
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"metadata": {},
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"outputs": [
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{

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