Radom Forest.md

import pandas as pd
import numpy as np
from matplotlib import pyplot as plt
import seaborn as sns

# importing csv file from local machine like r'path'
# reading in dataset with panda
df = pd.read_csv(r'C:\Users\kcoma\Jupyter Projects\DataSets\Iris Species\Iris\Iris.csv')
df.head()

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	Id	SepalLengthCm	SepalWidthCm	PetalLengthCm	PetalWidthCm	Species
0	1	5.1	3.5	1.4	0.2	Iris-setosa
1	2	4.9	3.0	1.4	0.2	Iris-setosa
2	3	4.7	3.2	1.3	0.2	Iris-setosa
3	4	4.6	3.1	1.5	0.2	Iris-setosa
4	5	5.0	3.6	1.4	0.2	Iris-setosa

# Dropping the 'Id' column from our dataframe (since we don't need an additional index).
df.drop("Id", axis=1, inplace=True)

# Viewing our modified dataframe.
df.head()

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	SepalLengthCm	SepalWidthCm	PetalLengthCm	PetalWidthCm	Species
0	5.1	3.5	1.4	0.2	Iris-setosa
1	4.9	3.0	1.4	0.2	Iris-setosa
2	4.7	3.2	1.3	0.2	Iris-setosa
3	4.6	3.1	1.5	0.2	Iris-setosa
4	5.0	3.6	1.4	0.2	Iris-setosa

# Importing the Label Encoder Class from sklearn
from sklearn.preprocessing import LabelEncoder

# Creating a new label Encoder object
# Encode target labels with value between 0 and n_classes-1.
encoder = LabelEncoder()

# Using the "fit_transform" method to label encode the 'Type' column 0: Private 1: Public In-State  2:Public Out-of-State
df["Species"] = encoder.fit_transform(df["Species"])

df.head(15)

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	SepalLengthCm	SepalWidthCm	PetalLengthCm	PetalWidthCm	Species
0	5.1	3.5	1.4	0.2	0
1	4.9	3.0	1.4	0.2	0
2	4.7	3.2	1.3	0.2	0
3	4.6	3.1	1.5	0.2	0
4	5.0	3.6	1.4	0.2	0
5	5.4	3.9	1.7	0.4	0
6	4.6	3.4	1.4	0.3	0
7	5.0	3.4	1.5	0.2	0
8	4.4	2.9	1.4	0.2	0
9	4.9	3.1	1.5	0.1	0
10	5.4	3.7	1.5	0.2	0
11	4.8	3.4	1.6	0.2	0
12	4.8	3.0	1.4	0.1	0
13	4.3	3.0	1.1	0.1	0
14	5.8	4.0	1.2	0.2	0

# importing the 'Train Test_split' function from scikit-learn 'model_selection' package
from sklearn.model_selection import train_test_split

# Splitting up our data dataframe into x and y subsets(x are the independ variables and y is the dependent variable types)
x_data = df.drop("Species",axis=1)
y_data = df["Species"]

# Retriveing X and Y train-test sets by calling the 'train_test_split' function (80-20 Split) 
x_train, x_test, y_train , y_test = train_test_split(x_data, y_data, test_size=0.2)

# Importing the RandomForestClassifier Class from Scikit-Learn's 'ensemble' package.
from sklearn.ensemble import RandomForestClassifier

# Creating a new classifier with twenty individual decision trees used to build the random forest.
model = RandomForestClassifier(n_estimators=20)

# Training our model on the X and Y train sets.
model.fit(x_train, y_train)

RandomForestClassifier(n_estimators=20)

model.score(x_test, y_test)

0.9666666666666667

# Examining the fratures for the independent variable 

# Feautures of little importance [Eliminated]
# Features of high Importance {Promoted}

# Printing out the importance for each independent variable (feature) with the "feature_importances_" field.
feature_imp = pd.Series(model.feature_importances_, index=["Sepal Length", "Sepal Width", "Petal Length", "Petal Width"]).sort_values(ascending=False)
feature_imp

# See which variables contribute the most and which contribute the least

Petal Length    0.488784
Petal Width     0.389083
Sepal Length    0.091857
Sepal Width     0.030277
dtype: float64

sns.barplot(x = feature_imp, y=feature_imp.index)

plt.title("Modeling the Importances of Each X Featues", pad=15,fontsize=15)
plt.xlabel("Importance Measures", fontsize=15)
plt.ylabel("Features", fontsize=15)

plt.show()

# According to the bar graph,"Sepal Length" & "Sepal Width" Represent a low importances with the featurs we have added
# We will drop the 3 columns: Sepal Length ,Sepal Width and Sepcies on the x variables, y variables will stay the same
# Dropping all non-predicate independent varialbes
x_data = df.drop(["SepalLengthCm", "SepalWidthCm","Species"],axis=1)

y_data = df["Species"]

# The Iris Table 
x_data.head()

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	PetalLengthCm	PetalWidthCm
0	1.4	0.2
1	1.4	0.2
2	1.3	0.2
3	1.5	0.2
4	1.4	0.2

# Performing our train-test split again on the new x and y datasets.
# Changed the test_size from 0.2 to 0.1 to get a higher accuracy rating
x_train, x_test, y_train, y_test = train_test_split(x_data, y_data, test_size=0.1)

# Fitting a Random Forest model to our new training data.
model = RandomForestClassifier(n_estimators=20)
model.fit(x_train, y_train)

# Printing out the accuracy measure of our model.
print("Model Accuracy: " + str(model.score(x_test, y_test)))

Model Accuracy: 1.0

# Importing the 'plot_tree' function (to be used for visualization purposes).
from sklearn.tree import plot_tree

# Setting the size of our figure to 60 by 30.
plt.figure(figsize=(60,30))

# Visualizing one of the decision trees from our Random Forest model.
plot_tree(model.estimators_[5], 
          filled=True,  # specifies whether or not the decision nodes should be filled appropriately or not.
          feature_names = ["PetalLengthCm", "PetalWidthCm"], # names of our independent variables.
          class_names = ["Iris-Setosa", "Iris-Versicolor", "Iris-Virginica"], # values of our dependent variable.
          rounded = True, # rounding probablities.
          proportion = False, # removing percentages.
          precision = 2); # setting number of significant figures to two.