100% Machine learning

# -*- coding: utf-8 -*-
"""
Created on Wed Nov 13 10:52:01 2019
%bhayya learnt this from codebasicshub.com
@author: Vikrambhayya

"""
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from sklearn import linear_model
df = pd.read_csv("C:\\Users\\Vikrambhayya\\Documents\\ML_b_1.csv")
df
""" matplotlib inline"""
plt.xlabel('Area (sq.ft)')
plt.ylabel('price (Rs)')
plt.scatter(df.Area,df.price)
"""plt.scatter(df.Area,df.price, color='red', marker ='+')"""
reg = linear_model.LinearRegression()
reg.fit(df[['Area']],df.price)

"""reg.coef_"""
"""reg.intercept_"""
d=pd.read_csv("C:/Users/Vikrambhayya/Documents/Areas_to_predict_b.csv")
d.head(3)
p=reg.predict(d)
d['prices_dharalu']=p
d.to_csv("C:\\Users\\Vikrambhayya\\Documents\\anchanA_7_b.csv")

"""d.to_csv("C:\\Users\\Vikrambhayya\\Documents\\anchanA_1_b.csv", index=False)"""



""" 100% working code above """

""" slight modification is done below (modified the 100% working code given above)

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from sklearn import linear_model
df = pd.read_csv("C:\\Users\\Vikrambhayya\\Documents\\ML_b_1.csv")
df
plt.xlabel('vaiShAlyamu', fontsize=20)
plt.ylabel('dhara', fontsize=20)
plt.scatter(df.Area,df.price, color='red', marker ='+')
plt.plot(df.Area,reg.predict(df[['Area']]), color='blue')
d=pd.read_csv("C:/Users/Vikrambhayya/Documents/Areas_to_predict_b.csv")
d.head(3)
p=reg.predict(d)
d['prices_dharalu']=p
d.to_csv("C:\\Users\\Vikrambhayya\\Documents\\anchanA_4_b.csv", index=False)

"""# -*- coding: utf-8 -*-
"""
Created on Wed Nov 13 14:18:20 2019

@author:learnt from 'Deep learning part 2 codebasics website:
"""

import numpy as np
import matplotlib.pyplot as plt
""" matplotlib inline"""
import keras
from keras.datasets import mnist

(x_train, y_train), (x_test, y_test) = mnist.load_data()

x_train.shape

x_test.shape

x_train[0]

plt.matshow(x_train[0])

y_train[0]

x_train=x_train/255

x_test=x_test/255

from keras.models import Sequential

from keras.layers import Dense, Activation, Flatten

model=Sequential()

model.add(Flatten(input_shape=[28,28]))

model.add(Dense(200, activation='relu'))

model.add(Dense(10, activation='softmax'))

model.summary()

model.compile(loss="sparse_categorical_crossentropy",optimizer="adam", metrics=["accuracy"])

model.fit(x_train,y_train,epochs=5)

plt.matshow(x_test[0])

x_test.shape

yp = model.predict(x_test)

yp[0]

np.argmax(yp[0])

plt.matshow(x_test[1])

yp[1]

np.argmax(yp[1])

model.evaluate(x_test,y_test)

नमस्कार / నమస్కారము /ನಮಸ್ಕಾರ/ வணக்கம்/ নমস্কাৰ/ નમસ્તે/ ସୁପ୍ରଭାତ/ നമസ്കാരം

learnt from codebasics website

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