# 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=10))

# Total number of records
n_records = data.shape[0]

# Number of records where individual's income is more than $50,000
n_greater_50k = np.sum([data['income'] == '>50K'])

# Number of records where individual's income is at most $50,000
n_at_most_50k = n_records - n_greater_50k

# Percentage of individuals whose income is more than $50,000
greater_percent = 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: {}%".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: 0.2478439697492371%

# 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)

# 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)

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

# Initialize a scaler, then apply it to the features
scaler = MinMaxScaler() # default=(0, 1)
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 = 50))

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

# Encode the 'income_raw' data to numerical values
income = (income_raw == '>50K').astype(int)

# 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)
income

103 total features after one-hot encoding.

0        0
1        0
2        0
3        0
4        0
        ..
45217    0
45218    0
45219    0
45220    0
45221    1
Name: income, Length: 45222, dtype: int64

# 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.

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 = TP / (TP + FP)
recall = TP / (TP + FN)
precision = TP / (TP + FP)

# TODO: Calculate F-score using the formula above for beta = 0.5 and correct values for precision and recall.
fscore = (1 + 0.5 * 0.5) * (precision * recall) / (0.5 * 0.5 * 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]

# Import two metrics from sklearn - fbeta_score and accuracy_score
from sklearn.metrics import fbeta_score, accuracy_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 = {}
    
    # Fit the learner to the training data using slicing with 'sample_size' using .fit(training_features[:], training_labels[:])
    start = time() # Get start time
    learner = learner.fit(X_train[:sample_size], y_train[:sample_size])
    end = time() # Get end time
    
    # Calculate the training time
    results['train_time'] = end - start
        
    # Get the predictions on the test set(X_test),
    #       then get predictions on the first 300 training samples(X_train) using .predict()
    start = time() # Get start time
    predictions_test = learner.predict(X_test)
    predictions_train = learner.predict(X_train[:300])
    end = time() # Get end time
    
    # Calculate the total prediction time
    results['pred_time'] = end - start
            
    # Compute accuracy on the first 300 training samples which is y_train[:300]
    results['acc_train'] = accuracy_score(y_train[:300], predictions_train)
        
    # Compute accuracy on test set using accuracy_score()
    results['acc_test'] = accuracy_score(y_test, predictions_test)
    
    # Compute F-score on the the first 300 training samples using fbeta_score()
    results['f_train'] = fbeta_score(y_train[:300], predictions_train, beta=0.5)
        
    # Compute F-score on the test set which is y_test
    results['f_test'] = fbeta_score(y_test, predictions_test, beta=0.5)
       
    # Success
    print("{} trained on {} samples.".format(learner.__class__.__name__, sample_size))
        
    # Return the results
    return results

# Import the three supervised learning models from sklearn
from sklearn.tree import DecisionTreeClassifier
from sklearn.ensemble import RandomForestClassifier
from sklearn.linear_model import LogisticRegression
# TODO: Initialize the three models
clf_A = DecisionTreeClassifier()
clf_B = RandomForestClassifier()
clf_C = LogisticRegression()

# Calculate the number of samples for 1%, 10%, and 100% of the training data

samples_100 = len(y_train)
samples_10 = int(0.1 * len(y_train))
samples_1 = int(0.01 * len(y_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.
DecisionTreeClassifier trained on 3617 samples.
DecisionTreeClassifier trained on 36177 samples.
RandomForestClassifier trained on 361 samples.
RandomForestClassifier trained on 3617 samples.
RandomForestClassifier trained on 36177 samples.
LogisticRegression trained on 361 samples.
LogisticRegression trained on 3617 samples.
LogisticRegression trained on 36177 samples.

/Users/lixinwei/Documents/GitHub/cd0025-supervised-learning/starter/visuals.py:124: UserWarning: Tight layout not applied. tight_layout cannot make Axes width small enough to accommodate all Axes decorations
  pl.tight_layout()

# Import 'GridSearchCV', 'make_scorer', and any other necessary libraries
from sklearn.model_selection import GridSearchCV
from sklearn.metrics import make_scorer
# Initialize the classifier
clf = RandomForestClassifier()

# Create the parameters list you wish to tune, using a dictionary if needed.
# HINT: parameters = {'parameter_1': [value1, value2], 'parameter_2': [value1, value2]}
parameters = {'n_estimators':[10,100,200,300,400,500]}

# Make an fbeta_score scoring object using make_scorer()
scorer = make_scorer(fbeta_score, beta=0.5)

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

# Fit the grid search object to the training data and find the optimal parameters using fit()
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: 0.8449
F-score on testing data: 0.6873

Optimized Model
------
Final accuracy score on the testing data: 0.8418
Final F-score on the testing data: 0.6798

# Import a supervised learning model that has 'feature_importances_'
from sklearn.ensemble import AdaBoostClassifier

# Train the supervised model on the training set using .fit(X_train, y_train)
model = AdaBoostClassifier()
model.fit(X_train, y_train)

# Extract the feature importances using .feature_importances_ 
importances = model.feature_importances_

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

/opt/anaconda3/lib/python3.12/site-packages/sklearn/ensemble/_weight_boosting.py:527: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning.
  warnings.warn(

# 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: 0.8418
F-score on testing data: 0.6798

Final Model trained on reduced data
------
Accuracy on testing data: 0.9235
F-score on testing data: 0.8424

	age	workclass	education_level	education-num	marital-status	occupation	relationship	race	sex	capital-gain	hours-per-week	native-country	income
0	39	State-gov	Bachelors	13.0	Never-married	Adm-clerical	Not-in-family	White	Male	2174.0	40.0	United-States	<=50K
1	50	Self-emp-not-inc	Bachelors	13.0	Married-civ-spouse	Exec-managerial	Husband	White	Male	0.0	13.0	United-States	<=50K
2	38	Private	HS-grad	9.0	Divorced	Handlers-cleaners	Not-in-family	White	Male	0.0	40.0	United-States	<=50K
3	53	Private	11th	7.0	Married-civ-spouse	Handlers-cleaners	Husband	Black	Male	0.0	40.0	United-States	<=50K
4	28	Private	Bachelors	13.0	Married-civ-spouse	Prof-specialty	Wife	Black	Female	0.0	40.0	Cuba	<=50K
5	37	Private	Masters	14.0	Married-civ-spouse	Exec-managerial	Wife	White	Female	0.0	40.0	United-States	<=50K
6	49	Private	9th	5.0	Married-spouse-absent	Other-service	Not-in-family	Black	Female	0.0	16.0	Jamaica	<=50K
7	52	Self-emp-not-inc	HS-grad	9.0	Married-civ-spouse	Exec-managerial	Husband	White	Male	0.0	45.0	United-States	>50K
8	31	Private	Masters	14.0	Never-married	Prof-specialty	Not-in-family	White	Female	14084.0	50.0	United-States	>50K
9	42	Private	Bachelors	13.0	Married-civ-spouse	Exec-managerial	Husband	White	Male	5178.0	40.0	United-States	>50K

	age	workclass	education_level	education-num	marital-status	occupation	relationship	race	sex	capital-gain	hours-per-week	native-country
0	0.301370	State-gov	Bachelors	0.800000	Never-married	Adm-clerical	Not-in-family	White	Male	0.667492	0.397959	United-States
1	0.452055	Self-emp-not-inc	Bachelors	0.800000	Married-civ-spouse	Exec-managerial	Husband	White	Male	0.000000	0.122449	United-States
2	0.287671	Private	HS-grad	0.533333	Divorced	Handlers-cleaners	Not-in-family	White	Male	0.000000	0.397959	United-States
3	0.493151	Private	11th	0.400000	Married-civ-spouse	Handlers-cleaners	Husband	Black	Male	0.000000	0.397959	United-States
4	0.150685	Private	Bachelors	0.800000	Married-civ-spouse	Prof-specialty	Wife	Black	Female	0.000000	0.397959	Cuba
5	0.273973	Private	Masters	0.866667	Married-civ-spouse	Exec-managerial	Wife	White	Female	0.000000	0.397959	United-States
6	0.438356	Private	9th	0.266667	Married-spouse-absent	Other-service	Not-in-family	Black	Female	0.000000	0.153061	Jamaica
7	0.479452	Self-emp-not-inc	HS-grad	0.533333	Married-civ-spouse	Exec-managerial	Husband	White	Male	0.000000	0.448980	United-States
8	0.191781	Private	Masters	0.866667	Never-married	Prof-specialty	Not-in-family	White	Female	0.829751	0.500000	United-States
9	0.342466	Private	Bachelors	0.800000	Married-civ-spouse	Exec-managerial	Husband	White	Male	0.742849	0.397959	United-States
10	0.273973	Private	Some-college	0.600000	Married-civ-spouse	Exec-managerial	Husband	Black	Male	0.000000	0.806122	United-States

Metric	Unoptimized Model	Optimized Model
Accuracy Score	0.8420	0.8431
F-score	0.6803	0.6827

CharityML Project: Finding Donors

Project Developer/Credit to: Xinwei Li

Finding Potential Donors: A Machine Learning Approach¶

Project Goal: Predicting Potential Donors¶

Exploring the Data¶

Conducting Data Exploration¶

Addressing Skewed Features¶

Normalizing Numerical Features¶

Handling Categorical Features¶

Splitting Data for Training and Validation¶

Evaluating Model Performance¶

Understanding Performance Metrics for Donor Prediction¶

Selecting Supervised Learning Models¶

Building an Efficient Model Evaluation Pipeline¶

Setting Up Model Evaluation¶

Optimizing Model Performance¶

Fine-Tuning the Selected Model¶

Results:¶

Feature Importance Analysis¶

Identifying Key Predictive Features¶

Evaluating Model Performance with Reduced Features¶

Conclusion¶

CharityML Project: Finding DonorsProject Developer/Credit to: Xinwei Li

Finding Potential Donors: A Machine Learning Approach¶

Project Goal: Predicting Potential Donors¶

Exploring the Data¶

Conducting Data Exploration¶

Addressing Skewed Features¶

Normalizing Numerical Features¶

Handling Categorical Features¶

Splitting Data for Training and Validation¶

Evaluating Model Performance¶

Understanding Performance Metrics for Donor Prediction¶

Selecting Supervised Learning Models¶

Building an Efficient Model Evaluation Pipeline¶

Setting Up Model Evaluation¶

Optimizing Model Performance¶

Fine-Tuning the Selected Model¶

Results:¶

Feature Importance Analysis¶

Identifying Key Predictive Features¶

Evaluating Model Performance with Reduced Features¶

Conclusion¶

CharityML Project: Finding Donors

Project Developer/Credit to: Xinwei Li