Image_Classifier_project.py

#!/usr/bin/env python
# coding: utf-8

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import pandas as pd
import numpy as np
from matplotlib import pyplot as ply
from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestClassifier
get_ipython().run_line_magic('matplotlib', 'inline')


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data=pd.read_csv('emnist.csv')
data.head()


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data.loc[3]


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#extracting the data
d=data.iloc[2,1:].values


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#reshaping the extracted data
d=d.reshape(28,28).astype('uint8')
ply.imshow(d)


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#separating label and pixels
df_x=data.iloc[:,1:]
df_y=data.iloc[:,0]


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#train and test the datas
x_train,x_test,y_train,y_test=train_test_split(df_x,df_y,test_size=0.2,random_state=4)


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#check data
x_train.head()


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y_train.head()


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#calling rf classifier
rf=RandomForestClassifier(n_estimators=100)


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#fit the model
rf.fit(x_train,y_train)


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#prediction test
pred=rf.predict(x_test)


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pred


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#check prediction accuracy
s=y_test.values

#calculate no of correct predictions
count=0
for i in range(len(pred)):
    if pred[i]==s[i]:
        count=count+1


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count


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#total values which was predicted
len(pred)


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len(pred)


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#accuracy
2785/3760