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%% Cell type:code id:6064e0b1 tags:
``` python
#Import scikit-learn dataset library
#from sklearn import datasets
from sklearn.model_selection import train_test_split
from sklearn.model_selection import cross_val_score
from sklearn import svm, metrics
from sklearn.ensemble import RandomForestClassifier
import pandas as pd
import numpy as np
from joblib import dump
#Read training datasets
df = pd.read_csv('H:\AI classification\TrainingDataMulti.csv', header=None)
# Print the head of csv document to check
print(df.head(1))
# The first 128 columns are features
df_feature = df.iloc[:, :128]
# the 129th column is labels
df_label = df.iloc[:, 128]
# Split dataset into training set and test set
X_train, X_test, y_train, y_test = train_test_split(df_feature, df_label, test_size=0.2) # 80% training and 20% test
```
%% Output
0 1 2 3 4 5 \
0 70.399324 127673.0908 -49.572308 127648.0176 -169.578319 127723.2374
6 7 8 9 ... 119 120 121 122 123 \
0 65.689611 605.91099 -57.003571 626.78553 ... 0 0 0 0 0
124 125 126 127 128
0 0 0 0 0 0
[1 rows x 129 columns]
%% Cell type:code id:cdc65331 tags:
``` python
# Create a Randomforest Classifier
clf1 = RandomForestClassifier(n_estimators=100, max_features=78)
# Train the model using the training sets
clf1.fit(X_train, y_train)
# #Predict the response for test dataset
y_pred1 = clf1.predict(X_test)
print("Accuracy:",metrics.accuracy_score(y_test, y_pred1))
```
%% Output
Accuracy: 0.9416666666666667
%% Cell type:code id:ae7d5339 tags:
``` python
# Using Cross-validation to evaluate classifier
scores1 = cross_val_score(clf1, X_train, y_train, cv=5)
#Print model's Scores
print("Scores", scores1)
print("Mean Scores", np.mean(scores1))
```
%% Output
Scores [0.94166667 0.95104167 0.934375 0.934375 0.921875 ]
Mean Scores 0.9366666666666668
%% Cell type:code id:253d7c20 tags:
``` python
#Dump the model
dump(clf1, 'H:/AI classification/RFC_part2.pkl')
```
%% Output
['H:/AI classification/RFC_part2.pkl']
%% Cell type:code id:943bcea4 tags:
``` python
# Load testing dataset
test_data=pd.read_csv('H:\AI classification\TestingDataMulti.csv', header=None)
# Predict Testing dataset
predictions = clf1.predict(test_data)
# Convert predictions into dataframe format
predictions_df = pd.DataFrame(predictions)
#Write the predictions to testing dataset
result = pd.concat([test_data,predictions_df], axis=1)
#Output a csv document
result.to_csv('H:/AI classification/test_pre2.csv', index = False, header = False)
```
%% Cell type:code id:8a8a1e04 tags:
``` python
# print precdictions
print(predictions)
```
%% Output
[2 2 2 2 2 2 1 1 2 2 2 1 1 1 1 1 2 1 1 1 1 1 2 2 2 2 0 0 0 0 0 0 0 1 1 1 1
1 2 2 1 1 2 2 2 2 2 2 2 1 2 2 2 1 2 2 2 1 1 2 1 1 1 1 1 1 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 0 0 0 0 0 0 0 0]
%% Cell type:code id:1521abed tags:
``` python
```
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