# Notebook from kmranrg/FindingDonorsForCharityML
Path: finding_donors.ipynb

# Machine Learning Engineer Nanodegree
## Supervised Learning
## Project: Finding Donors for *CharityML*_____no_output_____Welcome to the second project of the Machine Learning Engineer Nanodegree! In this notebook, some template code has already been provided for you, and it will be your job to implement the additional functionality necessary to successfully complete this project. Sections that begin with **'Implementation'** in the header indicate that the following block of code will require additional functionality which you must provide. Instructions will be provided for each section and the specifics of the implementation are marked in the code block with a `'TODO'` statement. Please be sure to read the instructions carefully!

In addition to implementing code, there will be questions that you must answer which relate to the project and your implementation. Each section where you will answer a question is preceded by a **'Question X'** header. Carefully read each question and provide thorough answers in the following text boxes that begin with **'Answer:'**. Your project submission will be evaluated based on your answers to each of the questions and the implementation you provide.

>**Note:** Please specify WHICH VERSION OF PYTHON you are using when submitting this notebook. Code and Markdown cells can be executed using the **Shift + Enter** keyboard shortcut. In addition, Markdown cells can be edited by typically double-clicking the cell to enter edit mode._____no_output_____## Getting Started

In this project, you will employ several supervised algorithms of your choice to accurately model individuals' income using data collected from the 1994 U.S. Census. You will then choose the best candidate algorithm from preliminary results and further optimize this algorithm to best model the data. Your goal with this implementation is to construct a model that accurately predicts whether an individual makes more than $50,000. This sort of task can arise in a non-profit setting, where organizations survive on donations.  Understanding an individual's income can help a non-profit better understand how large of a donation to request, or whether or not they should reach out to begin with.  While it can be difficult to determine an individual's general income bracket directly from public sources, we can (as we will see) infer this value from other publically available features.

The dataset for this project originates from the [UCI Machine Learning Repository](https://archive.ics.uci.edu/ml/datasets/Census+Income). The datset was donated by Ron Kohavi and Barry Becker, after being published in the article _"Scaling Up the Accuracy of Naive-Bayes Classifiers: A Decision-Tree Hybrid"_. You can find the article by Ron Kohavi [online](https://www.aaai.org/Papers/KDD/1996/KDD96-033.pdf). The data we investigate here consists of small changes to the original dataset, such as removing the `'fnlwgt'` feature and records with missing or ill-formatted entries._____no_output_____----
## Exploring the Data
Run the code cell below to load necessary Python libraries and load the census data. Note that the last column from this dataset, `'income'`, will be our target label (whether an individual makes more than, or at most, $50,000 annually). All other columns are features about each individual in the census database._____no_output_____

# Import libraries necessary for this project
import numpy as np
import pandas as pd
from time import time
from IPython.display import display # Allows the use of display() for DataFrames

# Import supplementary visualization code visuals.py
import visuals as vs

# Pretty display for notebooks
%matplotlib inline

# Load the Census dataset
data = pd.read_csv("census.csv")

# Success - Display the first record
display(data.head(n=1))_____no_output_____

### Implementation: Data Exploration
A cursory investigation of the dataset will determine how many individuals fit into either group, and will tell us about the percentage of these individuals making more than \$50,000. In the code cell below, you will need to compute the following:
- The total number of records, `'n_records'`
- The number of individuals making more than \$50,000 annually, `'n_greater_50k'`.
- The number of individuals making at most \$50,000 annually, `'n_at_most_50k'`.
- The percentage of individuals making more than \$50,000 annually, `'greater_percent'`.

**Hint:** You may need to look at the table above to understand how the `'income'` entries are formatted. _____no_output_____

# TODO: Total number of records
n_records = len(data)

# TODO: Number of records where individual's income is more than $50,000
n_greater_50k = len(data[data["income"] == ">50K"])

# TODO: Number of records where individual's income is at most $50,000
n_at_most_50k = len((data[data["income"] == "<=50K"]))

# TODO: Percentage of individuals whose income is more than $50,000
greater_percent = 100.00 * n_greater_50k / n_records

# Print the results
print("Total number of records: {}".format(n_records))
print("Individuals making more than $50,000: {}".format(n_greater_50k))
print("Individuals making at most $50,000: {}".format(n_at_most_50k))
print("Percentage of individuals making more than $50,000: {:.2f}%".format(greater_percent))Total number of records: 45222
Individuals making more than $50,000: 11208
Individuals making at most $50,000: 34014
Percentage of individuals making more than $50,000: 24.78%

----
## Preparing the Data
Before data can be used as input for machine learning algorithms, it often must be cleaned, formatted, and restructured — this is typically known as **preprocessing**. Fortunately, for this dataset, there are no invalid or missing entries we must deal with, however, there are some qualities about certain features that must be adjusted. This preprocessing can help tremendously with the outcome and predictive power of nearly all learning algorithms._____no_output_____### Transforming Skewed Continuous Features
A dataset may sometimes contain at least one feature whose values tend to lie near a single number, but will also have a non-trivial number of vastly larger or smaller values than that single number.  Algorithms can be sensitive to such distributions of values and can underperform if the range is not properly normalized. With the census dataset two features fit this description: '`capital-gain'` and `'capital-loss'`.

Run the code cell below to plot a histogram of these two features. Note the range of the values present and how they are distributed._____no_output_____

# Split the data into features and target label
income_raw = data['income']
features_raw = data.drop('income', axis = 1)

# Visualize skewed continuous features of original data
vs.distribution(data)_____no_output_____

For highly-skewed feature distributions such as `'capital-gain'` and `'capital-loss'`, it is common practice to apply a logarithmic transformation on the data so that the very large and very small values do not negatively affect the performance of a learning algorithm. Using a logarithmic transformation significantly reduces the range of values caused by outliers. Care must be taken when applying this transformation however: The logarithm of `0` is undefined, so we must translate the values by a small amount above `0` to apply the the logarithm successfully.

Run the code cell below to perform a transformation on the data and visualize the results. Again, note the range of values and how they are distributed. _____no_output_____

# Log-transform the skewed features
skewed = ['capital-gain', 'capital-loss']
features_log_transformed = pd.DataFrame(data = features_raw)
features_log_transformed[skewed] = features_raw[skewed].apply(lambda x: np.log(x + 1))

# Visualize the new log distributions
vs.distribution(features_log_transformed, transformed = True)_____no_output_____

### Normalizing Numerical Features
In addition to performing transformations on features that are highly skewed, it is often good practice to perform some type of scaling on numerical features. Applying a scaling to the data does not change the shape of each feature's distribution (such as `'capital-gain'` or `'capital-loss'` above); however, normalization ensures that each feature is treated equally when applying supervised learners. Note that once scaling is applied, observing the data in its raw form will no longer have the same original meaning, as exampled below.

Run the code cell below to normalize each numerical feature. We will use [`sklearn.preprocessing.MinMaxScaler`](http://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.MinMaxScaler.html) for this._____no_output_____

# Import sklearn.preprocessing.StandardScaler
from sklearn.preprocessing import MinMaxScaler

# Initialize a scaler, then apply it to the features
scaler = MinMaxScaler()
numerical = ['age', 'education-num', 'capital-gain', 'capital-loss', 'hours-per-week']

features_log_minmax_transform = pd.DataFrame(data = features_log_transformed)
features_log_minmax_transform[numerical] = scaler.fit_transform(features_log_transformed[numerical])

# Show an example of a record with scaling applied
display(features_log_minmax_transform.head(n = 1))_____no_output_____

### Implementation: Data Preprocessing

From the table in **Exploring the Data** above, we can see there are several features for each record that are non-numeric. Typically, learning algorithms expect input to be numeric, which requires that non-numeric features (called *categorical variables*) be converted. One popular way to convert categorical variables is by using the **one-hot encoding** scheme. One-hot encoding creates a _"dummy"_ variable for each possible category of each non-numeric feature. For example, assume `someFeature` has three possible entries: `A`, `B`, or `C`. We then encode this feature into `someFeature_A`, `someFeature_B` and `someFeature_C`.

|   | someFeature |                    | someFeature_A | someFeature_B | someFeature_C |
| :-: | :-: |                            | :-: | :-: | :-: |
| 0 |  B  |  | 0 | 1 | 0 |
| 1 |  C  | ----> one-hot encode ----> | 0 | 0 | 1 |
| 2 |  A  |  | 1 | 0 | 0 |

Additionally, as with the non-numeric features, we need to convert the non-numeric target label, `'income'` to numerical values for the learning algorithm to work. Since there are only two possible categories for this label ("<=50K" and ">50K"), we can avoid using one-hot encoding and simply encode these two categories as `0` and `1`, respectively. In code cell below, you will need to implement the following:
 - Use [`pandas.get_dummies()`](http://pandas.pydata.org/pandas-docs/stable/generated/pandas.get_dummies.html?highlight=get_dummies#pandas.get_dummies) to perform one-hot encoding on the `'features_raw'` data.
 - Convert the target label `'income_raw'` to numerical entries.
   - Set records with "<=50K" to `0` and records with ">50K" to `1`._____no_output_____

# TODO: One-hot encode the 'features_raw' data using pandas.get_dummies()
features_final = pd.get_dummies(features_log_minmax_transform)

# TODO: Encode the 'income_raw' data to numerical values
income = (income_raw == ">50K").astype(np.uint8)

# Print the number of features after one-hot encoding
encoded = list(features_final.columns)
print("{} total features after one-hot encoding.".format(len(encoded)))

# Uncomment the following line to see the encoded feature names
print(encoded)103 total features after one-hot encoding.
['age', 'education-num', 'capital-gain', 'capital-loss', 'hours-per-week', 'workclass_ Federal-gov', 'workclass_ Local-gov', 'workclass_ Private', 'workclass_ Self-emp-inc', 'workclass_ Self-emp-not-inc', 'workclass_ State-gov', 'workclass_ Without-pay', 'education_level_ 10th', 'education_level_ 11th', 'education_level_ 12th', 'education_level_ 1st-4th', 'education_level_ 5th-6th', 'education_level_ 7th-8th', 'education_level_ 9th', 'education_level_ Assoc-acdm', 'education_level_ Assoc-voc', 'education_level_ Bachelors', 'education_level_ Doctorate', 'education_level_ HS-grad', 'education_level_ Masters', 'education_level_ Preschool', 'education_level_ Prof-school', 'education_level_ Some-college', 'marital-status_ Divorced', 'marital-status_ Married-AF-spouse', 'marital-status_ Married-civ-spouse', 'marital-status_ Married-spouse-absent', 'marital-status_ Never-married', 'marital-status_ Separated', 'marital-status_ Widowed', 'occupation_ Adm-clerical', 'occupation_ Armed-Forces', 'occupation_ Craft-repair', 'occupation_ Exec-managerial', 'occupation_ Farming-fishing', 'occupation_ Handlers-cleaners', 'occupation_ Machine-op-inspct', 'occupation_ Other-service', 'occupation_ Priv-house-serv', 'occupation_ Prof-specialty', 'occupation_ Protective-serv', 'occupation_ Sales', 'occupation_ Tech-support', 'occupation_ Transport-moving', 'relationship_ Husband', 'relationship_ Not-in-family', 'relationship_ Other-relative', 'relationship_ Own-child', 'relationship_ Unmarried', 'relationship_ Wife', 'race_ Amer-Indian-Eskimo', 'race_ Asian-Pac-Islander', 'race_ Black', 'race_ Other', 'race_ White', 'sex_ Female', 'sex_ Male', 'native-country_ Cambodia', 'native-country_ Canada', 'native-country_ China', 'native-country_ Columbia', 'native-country_ Cuba', 'native-country_ Dominican-Republic', 'native-country_ Ecuador', 'native-country_ El-Salvador', 'native-country_ England', 'native-country_ France', 'native-country_ Germany', 'native-country_ Greece', 'native-country_ Guatemala', 'native-country_ Haiti', 'native-country_ Holand-Netherlands', 'native-country_ Honduras', 'native-country_ Hong', 'native-country_ Hungary', 'native-country_ India', 'native-country_ Iran', 'native-country_ Ireland', 'native-country_ Italy', 'native-country_ Jamaica', 'native-country_ Japan', 'native-country_ Laos', 'native-country_ Mexico', 'native-country_ Nicaragua', 'native-country_ Outlying-US(Guam-USVI-etc)', 'native-country_ Peru', 'native-country_ Philippines', 'native-country_ Poland', 'native-country_ Portugal', 'native-country_ Puerto-Rico', 'native-country_ Scotland', 'native-country_ South', 'native-country_ Taiwan', 'native-country_ Thailand', 'native-country_ Trinadad&Tobago', 'native-country_ United-States', 'native-country_ Vietnam', 'native-country_ Yugoslavia']

### Shuffle and Split Data
Now all _categorical variables_ have been converted into numerical features, and all numerical features have been normalized. As always, we will now split the data (both features and their labels) into training and test sets. 80% of the data will be used for training and 20% for testing.

Run the code cell below to perform this split._____no_output_____

# Import train_test_split
from sklearn.model_selection import train_test_split

# Split the 'features' and 'income' data into training and testing sets
X_train, X_test, y_train, y_test = train_test_split(features_final, income, test_size = 0.2, random_state = 0)

# Show the results of the split
print("Training set has {} samples.".format(X_train.shape[0]))
print("Testing set has {} samples.".format(X_test.shape[0]))Training set has 36177 samples.
Testing set has 9045 samples.

----
## Evaluating Model Performance
In this section, we will investigate four different algorithms, and determine which is best at modeling the data. Three of these algorithms will be supervised learners of your choice, and the fourth algorithm is known as a *naive predictor*._____no_output_____### Metrics and the Naive Predictor
*CharityML*, equipped with their research, knows individuals that make more than \$50,000 are most likely to donate to their charity. Because of this, *CharityML* is particularly interested in predicting who makes more than \$50,000 accurately. It would seem that using **accuracy** as a metric for evaluating a particular model's performace would be appropriate. Additionally, identifying someone that *does not* make more than \$50,000 as someone who does would be detrimental to *CharityML*, since they are looking to find individuals willing to donate. Therefore, a model's ability to precisely predict those that make more than \$50,000 is *more important* than the model's ability to **recall** those individuals. We can use **F-beta score** as a metric that considers both precision and recall:

$$ F_{\beta} = (1 + \beta^2) \cdot \frac{precision \cdot recall}{\left( \beta^2 \cdot precision \right) + recall} $$

In particular, when $\beta = 0.5$, more emphasis is placed on precision. This is called the **F$_{0.5}$ score** (or F-score for simplicity).

Looking at the distribution of classes (those who make at most \$50,000, and those who make more), it's clear most individuals do not make more than \$50,000. This can greatly affect **accuracy**, since we could simply say *"this person does not make more than \$50,000"* and generally be right, without ever looking at the data! Making such a statement would be called **naive**, since we have not considered any information to substantiate the claim. It is always important to consider the *naive prediction* for your data, to help establish a benchmark for whether a model is performing well. That been said, using that prediction would be pointless: If we predicted all people made less than \$50,000, *CharityML* would identify no one as donors. _____no_output_____### Question 1 - Naive Predictor Performace
*If we chose a model that always predicted an individual made more than \$50,000, what would that model's accuracy and F-score be on this dataset?*
**Note:** You must use the code cell below and assign your results to `'accuracy'` and `'fscore'` to be used later._____no_output_____

'''
TP = np.sum(income) # Counting the ones as this is the naive case. Note that 'income' is the 'income_raw' data
encoded to numerical values done in the data preprocessing step.
FP = income.count() - TP # Specific to the naive case

TN = 0 # No predicted negatives in the naive case
FN = 0 # No predicted negatives in the naive case
'''
# TODO: Calculate accuracy, precision and recall
accuracy = (np.sum(income)) / ((np.sum(income)) + float(((income.count()) - np.sum(income))))
recall = np.sum(income) / float((np.sum(income) + 0))
precision = np.sum(income) / float(((np.sum(income) + ((income.count()) - np.sum(income)))))
beta = 0.5

# TODO: Calculate F-score using the formula above for beta = 0.5 and correct values for precision and recall.
# HINT: The formula above can be written as (1 + beta**2) * (precision * recall) / ((beta**2 * precision) + recall)
fscore = (1+ beta**2) * (precision * recall) / ((beta ** 2 * precision) + recall)

# Print the results

print("Naive Predictor: [Accuracy score: {:.4f}, F-score: {:.4f}]".format(accuracy, fscore))Naive Predictor: [Accuracy score: 0.2478, F-score: 0.2917]

###  Supervised Learning Models
**The following supervised learning models are currently available in** [`scikit-learn`](http://scikit-learn.org/stable/supervised_learning.html) **that you may choose from:**
- Gaussian Naive Bayes (GaussianNB)
- Decision Trees
- Ensemble Methods (Bagging, AdaBoost, Random Forest, Gradient Boosting)
- K-Nearest Neighbors (KNeighbors)
- Stochastic Gradient Descent Classifier (SGDC)
- Support Vector Machines (SVM)
- Logistic Regression_____no_output_____### Question 2 - Model Application
List three of the supervised learning models above that are appropriate for this problem that you will test on the census data. For each model chosen
- *Describe one real-world application in industry where the model can be applied.* (You may need to do research for this — give references!)
- *What are the strengths of the model; when does it perform well?*
- *What are the weaknesses of the model; when does it perform poorly?*
- *What makes this model a good candidate for the problem, given what you know about the data?*_____no_output_____**Answer: **

- Decision Tree
    - Use of tree based classification for drug analysis can be found in [K.T. DAGO, R. LUTHRINGER, R. LENGELLE, G. RINAUDO, AND J. P. MATCHER. Statistical decision tree: A tool for studying pharmaco-EEG effects of CNS-active drugs. Neuropsychobiology, 29(2):91--96, 1994.]
    - Strengths
        - easy to understand
        - easy for anyone to code, visualize, manipulate, and explain
    - Weakness
        - extremely sensitive to small perturbations in the data
        - can easily overfit
        - can have problems out-of-sample prediction
    - Our dataset is dominated by categorical features and this method is easy to understand.
- SVC
    - SVM's have been used in the prediction of bankruptcy of firms. [http://www.svms.org/finance/ShinLeeKim2005.pdf]
    - Strengths
        - Kernel can be adjusted to come up with a better classifier.
        - efficient algorithm as it is based on sequential minimal optimization.
    - Weakness
        - there is a tuning parameter and the kernel models can be sensitive to overfitting
    - For our classification problem will not be linearly separable, and for that we can use a modified kernel.
- Ensemble Method : Random Forest
    - Good Real world example is Classification of Urban Remote Sensing Images [https://www.researchgate.net/publication/3204218_Decision_Fusion_for_the_Classification_of_Urban_Remote_Sensing_Images]
    - Strengths
        - can handle missing values
        - little to no tweaking required
        - can work in parallel
    - Weakness
        - bias for multiclass problems towards more recurring classes
        - difficulty in interpreting the model
    - Since, Random Forest are great on almost any machine learning problem, and will not get overfit like decision tree, I think it would be appropriate along with the fact that our dataset has categorical features._____no_output_____### Implementation - Creating a Training and Predicting Pipeline
To properly evaluate the performance of each model you've chosen, it's important that you create a training and predicting pipeline that allows you to quickly and effectively train models using various sizes of training data and perform predictions on the testing data. Your implementation here will be used in the following section.
In the code block below, you will need to implement the following:
 - Import `fbeta_score` and `accuracy_score` from [`sklearn.metrics`](http://scikit-learn.org/stable/modules/classes.html#sklearn-metrics-metrics).
 - Fit the learner to the sampled training data and record the training time.
 - Perform predictions on the test data `X_test`, and also on the first 300 training points `X_train[:300]`.
   - Record the total prediction time.
 - Calculate the accuracy score for both the training subset and testing set.
 - Calculate the F-score for both the training subset and testing set.
   - Make sure that you set the `beta` parameter!_____no_output_____

# TODO: Import two metrics from sklearn - fbeta_score and accuracy_score
from sklearn.metrics import accuracy_score, fbeta_score

def train_predict(learner, sample_size, X_train, y_train, X_test, y_test):
    '''
    inputs:
       - learner: the learning algorithm to be trained and predicted on
       - sample_size: the size of samples (number) to be drawn from training set
       - X_train: features training set
       - y_train: income training set
       - X_test: features testing set
       - y_test: income testing set
    '''

    results = {}

    # TODO: Fit the learner to the training data using slicing with 'sample_size'
    start = time() # Get start time
    learner = learner.fit(X_train.iloc[:sample_size,:],y_train.iloc[:sample_size])
    end = time() # Get end time

    # TODO: Calculate the training time
    results['train_time'] = end - start

    # TODO: Get the predictions on the test set,
    #       then get predictions on the first 300 training samples
    start = time() # Get start time
    predictions_test = learner.predict(X_test)
    predictions_train = learner.predict(X_train[:300])
    end = time() # Get end time

    # TODO: Calculate the total prediction time
    results['pred_time'] = end-start

    # TODO: Compute accuracy on the first 300 training samples
    results['acc_train'] = accuracy_score(y_train[:300], predictions_train[:300])

    # TODO: Compute accuracy on test set
    results['acc_test'] = accuracy_score(y_test, predictions_test)

    # TODO: Compute F-score on the the first 300 training samples
    results['f_train'] = fbeta_score(y_train[:300], predictions_train[:300], beta=0.5)

    # TODO: Compute F-score on the test set
    results['f_test'] = fbeta_score(y_test, predictions_test, beta=0.5)

    # Success
    print("{} trained on {} samples in {}.".format(learner.__class__.__name__, sample_size, results['train_time']))

    # Return the results
    return results_____no_output_____

### Implementation: Initial Model Evaluation
In the code cell, you will need to implement the following:
- Import the three supervised learning models you've discussed in the previous section.
- Initialize the three models and store them in `'clf_A'`, `'clf_B'`, and `'clf_C'`.
  - Use a `'random_state'` for each model you use, if provided.
  - **Note:** Use the default settings for each model — you will tune one specific model in a later section.
- Calculate the number of records equal to 1%, 10%, and 100% of the training data.
  - Store those values in `'samples_1'`, `'samples_10'`, and `'samples_100'` respectively.

**Note:** Depending on which algorithms you chose, the following implementation may take some time to run!_____no_output_____

# TODO: Import the three supervised learning models from sklearn
from sklearn.tree import DecisionTreeClassifier
from sklearn.svm import SVC
from sklearn.ensemble import RandomForestClassifier

# TODO: Initialize the three models
clf_A = DecisionTreeClassifier(random_state=0)
clf_B = SVC(random_state=0)
clf_C = RandomForestClassifier(random_state=0)

# TODO: Calculate the number of samples for 1%, 10%, and 100% of the training data
samples_1 = int(.01*len(X_train))
samples_10 = int(.1*len(X_train))
samples_100 = int(len(X_train))

# Collect results on the learners
results = {}
for clf in [clf_A, clf_B, clf_C]:
    clf_name = clf.__class__.__name__
    results[clf_name] = {}
    for i, samples in enumerate([samples_1, samples_10, samples_100]):
        results[clf_name][i] = \
        train_predict(clf, samples, X_train, y_train, X_test, y_test)

# Run metrics visualization for the three supervised learning models chosen
vs.evaluate(results, accuracy, fscore)DecisionTreeClassifier trained on 361 samples in 0.0124678611755.
DecisionTreeClassifier trained on 3617 samples in 0.0259101390839.
DecisionTreeClassifier trained on 36177 samples in 0.445218086243.

----
## Improving Results
In this final section, you will choose from the three supervised learning models the *best* model to use on the student data. You will then perform a grid search optimization for the model over the entire training set (`X_train` and `y_train`) by tuning at least one parameter to improve upon the untuned model's F-score. _____no_output_____### Question 3 - Choosing the Best Model
*Based on the evaluation you performed earlier, in one to two paragraphs, explain to *CharityML* which of the three models you believe to be most appropriate for the task of identifying individuals that make more than \$50,000.*
**Hint:** Your answer should include discussion of the metrics, prediction/training time, and the algorithm's suitability for the data._____no_output_____**Answer: **

The Decision Tree classifier performs much better on the training set in terms of the accuracy and F-Score, although, the Random Forest classifier generalizes well by demonstrating better accuracy and F-score on the test set.

Also, comparing the times taken for prediction/training, we can observe that even with the large amount of time taken by the SVM, it offered no visible benefit in terms of accuracy. Finally, considering the more time taken by the RandomForest classifier, it offers us better accuracy which is critical for CharityML.

Therefore, I choose to select the RandomForest Classifier._____no_output_____### Question 4 - Describing the Model in Layman's Terms
*In one to two paragraphs, explain to *CharityML*, in layman's terms, how the final model chosen is supposed to work. Be sure that you are describing the major qualities of the model, such as how the model is trained and how the model makes a prediction. Avoid using advanced mathematical or technical jargon, such as describing equations or discussing the algorithm implementation.*_____no_output_____**Answer: **

Decision trees identify the features that are most discerning when it comes to identifying classes. We use it for modelling decision rules for a specific classification problem where each node acts as a decision using a certain feature to divide the instances such that the purity between the two groups is maximized.

The idea behind random forest is to generate multiple smaller trees from random subsets of our dataset which gives us biased classifiers instead of a very deep tree. Then we aggregate the output of these shallow trees and thereby get a final classification which is more robust.

**Explanation in terms of Layman:**

Suppose you’re very indecisive, so whenever you want to watch a movie, you ask your friend Willow if she thinks you’ll like it. In order to answer, Willow first needs to figure out what movies you like, so you give her a bunch of movies and tell her whether you liked each one or not (i.e., you give her a labeled training set). Then, when you ask her if she thinks you’ll like movie X or not, she plays a 20 questions-like game with IMDB, asking questions like “Is X a romantic movie?”, “Does Johnny Depp star in X?”, and so on. She asks more informative questions first (i.e., she maximizes the information gain of each question), and gives you a yes/no answer at the end.

Thus, Willow is a decision tree for your movie preferences.

But Willow is only human, so she doesn’t always generalize your preferences very well (i.e., she overfits). In order to get more accurate recommendations, you’d like to ask a bunch of your friends, and watch movie X if most of them say they think you’ll like it. That is, instead of asking only Willow, you want to ask Woody, Apple, and Cartman as well, and they vote on whether you’ll like a movie (i.e., you build an ensemble classifier, aka a forest in this case).

Now you don’t want each of your friends to do the same thing and give you the same answer, so you first give each of them slightly different data. After all, you’re not absolutely sure of your preferences yourself – you told Willow you loved Titanic, but maybe you were just happy that day because it was your birthday, so maybe some of your friends shouldn’t use the fact that you liked Titanic in making their recommendations. Or maybe you told her you loved Cinderella, but actually you really really loved it, so some of your friends should give Cinderella more weight. So instead of giving your friends the same data you gave Willow, you give them slightly perturbed versions. You don’t change your love/hate decisions, you just say you love/hate some movies a little more or less (formally, you give each of your friends a bootstrapped version of your original training data). For example, whereas you told Willow that you liked Black Swan and Harry Potter and disliked Avatar, you tell Woody that you liked Black Swan so much you watched it twice, you disliked Avatar, and don’t mention Harry Potter at all.

By using this ensemble, you hope that while each of your friends gives somewhat idiosyncratic recommendations (Willow thinks you like vampire movies more than you do, Woody thinks you like Pixar movies, and Cartman thinks you just hate everything), the errors get canceled out in the majority. Thus, your friends now form a bagged (bootstrap aggregated) forest of your movie preferences.

There’s still one problem with your data, however. While you loved both Titanic and Inception, it wasn’t because you like movies that star Leonardio DiCaprio. Maybe you liked both movies for other reasons. Thus, you don’t want your friends to all base their recommendations on whether Leo is in a movie or not. So when each friend asks IMDB a question, only a random subset of the possible questions is allowed (i.e., when you’re building a decision tree, at each node you use some randomness in selecting the attribute to split on, say by randomly selecting an attribute or by selecting an attribute from a random subset). This means your friends aren’t allowed to ask whether Leonardo DiCaprio is in the movie whenever they want. So whereas previously you injected randomness at the data level, by perturbing your movie preferences slightly, now you’re injecting randomness at the model level, by making your friends ask different questions at different times.

And so now our friends form a random forest._____no_output_____### Implementation: Model Tuning
Fine tune the chosen model. Use grid search (`GridSearchCV`) with at least one important parameter tuned with at least 3 different values. You will need to use the entire training set for this. In the code cell below, you will need to implement the following:
- Import [`sklearn.grid_search.GridSearchCV`](http://scikit-learn.org/0.17/modules/generated/sklearn.grid_search.GridSearchCV.html) and [`sklearn.metrics.make_scorer`](http://scikit-learn.org/stable/modules/generated/sklearn.metrics.make_scorer.html).
- Initialize the classifier you've chosen and store it in `clf`.
 - Set a `random_state` if one is available to the same state you set before.
- Create a dictionary of parameters you wish to tune for the chosen model.
 - Example: `parameters = {'parameter' : [list of values]}`.
 - **Note:** Avoid tuning the `max_features` parameter of your learner if that parameter is available!
- Use `make_scorer` to create an `fbeta_score` scoring object (with $\beta = 0.5$).
- Perform grid search on the classifier `clf` using the `'scorer'`, and store it in `grid_obj`.
- Fit the grid search object to the training data (`X_train`, `y_train`), and store it in `grid_fit`.

**Note:** Depending on the algorithm chosen and the parameter list, the following implementation may take some time to run!_____no_output_____

# TODO: Import 'GridSearchCV', 'make_scorer', and any other necessary libraries
from sklearn.model_selection import GridSearchCV
from sklearn.metrics import make_scorer

# TODO: Initialize the classifier
clf = RandomForestClassifier(random_state=0)

# TODO: Create the parameters list you wish to tune
parameters = {'n_estimators': [10,100,500,1000,2000], 'criterion': ['gini', 'entropy']}
# TODO: Make an fbeta_score scoring object
scorer = make_scorer(fbeta_score, beta=0.5)

# TODO: Perform grid search on the classifier using 'scorer' as the scoring method
grid_obj = GridSearchCV(clf,parameters,scoring=scorer)

# TODO: Fit the grid search object to the training data and find the optimal parameters
grid_fit = grid_obj.fit(X_train,y_train)

# Get the estimator
best_clf = grid_fit.best_estimator_

# Make predictions using the unoptimized and model
predictions = (clf.fit(X_train, y_train)).predict(X_test)
best_predictions = best_clf.predict(X_test)

# Report the before-and-afterscores
print("Unoptimized model\n------")
print("Accuracy score on testing data: {:.4f}".format(accuracy_score(y_test, predictions)))
print("F-score on testing data: {:.4f}".format(fbeta_score(y_test, predictions, beta = 0.5)))
print("\nOptimized Model\n------")
print("Final accuracy score on the testing data: {:.4f}".format(accuracy_score(y_test, best_predictions)))
print("Final F-score on the testing data: {:.4f}".format(fbeta_score(y_test, best_predictions, beta = 0.5)))Unoptimized model
------
Accuracy score on testing data: 0.8377
F-score on testing data: 0.6715

Optimized Model
------
Final accuracy score on the testing data: 0.8434
Final F-score on the testing data: 0.6835

### Question 5 - Final Model Evaluation
_What is your optimized model's accuracy and F-score on the testing data? Are these scores better or worse than the unoptimized model? How do the results from your optimized model compare to the naive predictor benchmarks you found earlier in **Question 1**?_
**Note:** Fill in the table below with your results, and then provide discussion in the **Answer** box._____no_output_____

start = time()
best_clf.predict(X_test)
end = time()

print("clf took " + str(end-start))

start = time()
best_clf.predict(X_test)
end = time()

print("best_clf took " + str(end-start))
clf took 7.30475497246
best_clf took 7.05935311317
clf_____no_output_____best_clf_____no_output_____

#### Results:

|     Metric     | Benchmark Predictor | Unoptimized Model | Optimized Model |
| :------------: | :-----------------: | :---------------: | :-------------: |
| Accuracy Score |       0.2478        |      0.8377       |     0.8434      |
| F-score        |       0.2917        |      0.6715       |     0.6835      |

_____no_output_____**Answer: **

Yes, it is clear that the optimized model scores are better than the unoptimized model scores and they are much better than benchmark scores._____no_output_____----
## Feature Importance

An important task when performing supervised learning on a dataset like the census data we study here is determining which features provide the most predictive power. By focusing on the relationship between only a few crucial features and the target label we simplify our understanding of the phenomenon, which is most always a useful thing to do. In the case of this project, that means we wish to identify a small number of features that most strongly predict whether an individual makes at most or more than \$50,000.

Choose a scikit-learn classifier (e.g., adaboost, random forests) that has a `feature_importance_` attribute, which is a function that ranks the importance of features according to the chosen classifier.  In the next python cell fit this classifier to training set and use this attribute to determine the top 5 most important features for the census dataset._____no_output_____### Question 6 - Feature Relevance Observation
When **Exploring the Data**, it was shown there are thirteen available features for each individual on record in the census data.
_Of these thirteen records, which five features do you believe to be most important for prediction, and in what order would you rank them and why?______no_output_____**Answer:**

In my opinion, age would be the best indicator as older people have a higher capacity for donation to charities since they have fewer responsibilities. After that, I think marital status would be important as couples tend to be in a more stable financial situation and are therefore more likely to donate. Then would be the hours per week as longer hours would indicate higher salaries. Fourth, I would think would be education level as people who have higher education degrees, tend to be more financially stable. Finally, I think occupation would be a good indicator as some occupations tend to have higher salaries where as some do not.

So my prediction would be:

Age > Marital Status > Hours per Week > Education Level >  Occupation_____no_output_____### Implementation - Extracting Feature Importance
Choose a `scikit-learn` supervised learning algorithm that has a `feature_importance_` attribute availble for it. This attribute is a function that ranks the importance of each feature when making predictions based on the chosen algorithm.

In the code cell below, you will need to implement the following:
 - Import a supervised learning model from sklearn if it is different from the three used earlier.
 - Train the supervised model on the entire training set.
 - Extract the feature importances using `'.feature_importances_'`._____no_output_____

# TODO: Import a supervised learning model that has 'feature_importances_'
# TODO: Train the supervised model on the training set

# Since, I already trained a RandomForest with our dataset and it has feature_importances_,
# I will re use the best_clf
model = best_clf

# TODO: Extract the feature importances
importances = model.feature_importances_

# Plot
vs.feature_plot(importances, X_train, y_train)_____no_output_____

### Question 7 - Extracting Feature Importance

Observe the visualization created above which displays the five most relevant features for predicting if an individual makes at most or above \$50,000.
_How do these five features compare to the five features you discussed in **Question 6**? If you were close to the same answer, how does this visualization confirm your thoughts? If you were not close, why do you think these features are more relevant?______no_output_____**Answer:**

In Q6, I predicted Age > Marital Status > Hours per Week > Education Level >  Occupation

I correctly predicted age to be the most influential while Marital Status, Hours per week also feature in the top 5 in the visualization.

For Education and Occupation, I think, that since, the Random Forest Classifier uses Decision Trees, it was able to better use education-num instead of education level since education-num was a number to get better purity and therefore it features instead of the education level.

Also, capital gain is also another metric that I did not think of it earlier but thinking again, it seems that capital-gain would be a good parameter to predict the cash in hand. Hence, even if two people having the same occupation have different income, they might not donate hence Capital Gain would be a better metric than occupation

_____no_output_____### Feature Selection
How does a model perform if we only use a subset of all the available features in the data? With less features required to train, the expectation is that training and prediction time is much lower — at the cost of performance metrics. From the visualization above, we see that the top five most important features contribute more than half of the importance of **all** features present in the data. This hints that we can attempt to *reduce the feature space* and simplify the information required for the model to learn. The code cell below will use the same optimized model you found earlier, and train it on the same training set *with only the top five important features*. _____no_output_____

# Import functionality for cloning a model
from sklearn.base import clone

# Reduce the feature space
X_train_reduced = X_train[X_train.columns.values[(np.argsort(importances)[::-1])[:5]]]
X_test_reduced = X_test[X_test.columns.values[(np.argsort(importances)[::-1])[:5]]]

# Train on the "best" model found from grid search earlier
clf = (clone(best_clf)).fit(X_train_reduced, y_train)

# Make new predictions
reduced_predictions = clf.predict(X_test_reduced)

# Report scores from the final model using both versions of data
print "Final Model trained on full data\n------"
print "Accuracy on testing data: {:.4f}".format(accuracy_score(y_test, best_predictions))
print "F-score on testing data: {:.4f}".format(fbeta_score(y_test, best_predictions, beta = 0.5))
print "\nFinal Model trained on reduced data\n------"
print "Accuracy on testing data: {:.4f}".format(accuracy_score(y_test, reduced_predictions))
print "F-score on testing data: {:.4f}".format(fbeta_score(y_test, reduced_predictions, beta = 0.5))Final Model trained on full data
------
Accuracy on testing data: 0.8434
F-score on testing data: 0.6835

Final Model trained on reduced data
------
Accuracy on testing data: 0.8369
F-score on testing data: 0.6712

### Question 8 - Effects of Feature Selection
*How does the final model's F-score and accuracy score on the reduced data using only five features compare to those same scores when all features are used?*
*If training time was a factor, would you consider using the reduced data as your training set?*_____no_output_____**Modified Answer:**

The accuracy is not decreased much but F-score decresed significantly. So, if training time was a factor, I would not reduce the data, because there is almost 30% decrease in F-score.  _____no_output_____> **Note**: Once you have completed all of the code implementations and successfully answered each question above, you may finalize your work by exporting the iPython Notebook as an HTML document. You can do this by using the menu above and navigating to
**File -> Download as -> HTML (.html)**. Include the finished document along with this notebook as your submission._____no_output_____