grohith327

import keras
from keras.datasets import mnist
from keras.models import Sequential
from keras.layers import Dense, Dropout, Flatten
from keras.layers import Conv2D, MaxPooling2D
from keras import backend as K

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

img_rows, img_cols = 28, 28

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from tpot import TPOTClassifier
from sklearn.datasets import load_digits
from sklearn.model_selection import train_test_split

digits = load_digits()
x_train, x_test, y_train, y_test = train_test_split(digits.data,digits.target,train_size=0.75,test_size=0.25)

clf = TPOTClassifier(generations=10, population_size=20, verbosity=2)
clf.fit(x_train,y_train)
print(clf.score(x_test,y_test))

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import pandas as pd
import numpy as np

df = pd.read_csv("/Users/rohith/Documents/Datasets/Tesla_Stock_prices/Tesla_Stock.csv") # read csv file

rows = df.values.tolist()  # convert dataframe into a list
rows.reverse()

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from sklearn.model_selection import train_test_split
x_train = []
y_train = []
x_test = []
y_test = []
X = []
Y = []
for row in rows:
    X.append(int(''.join(row[0].split('/'))))
    Y.append(row[3])

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# Linear Regression model
from sklearn.linear_model import LinearRegression 
clf_lr = LinearRegression()
clf_lr.fit(x_train,y_train)
y_pred_lr = clf_lr.predict(x_test)

# Support Vector Machine with a Radial Basis Function as kernel 
from sklearn.svm import SVR
clf_svr = SVR(kernel='rbf', C=1e3, gamma=0.1)
clf_svr.fit(x_train,y_train)

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import matplotlib.pyplot as plt 

f,(ax1,ax2) = plt.subplots(1,2,figsize=(30,10))

# Linear Regression
ax1.scatter(range(len(y_test)),y_test,label='data')
ax1.plot(range(len(y_test)),y_pred_lr,color='green',label='LR model')
ax1.legend()

# Support Vector Machine

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print("Accuracy of Linear Regerssion Model:",clf_lr.score(x_test,y_test))
print("Accuracy of SVM-RBF Model:",clf_svr.score(x_test,y_test))
print("Accuracy of Random Forest Model:",clf_rf.score(x_test,y_test))
print("Accuracy of Gradient Boosting Model:",clf_gb.score(x_test,y_test))

grohith327

import keras
from keras.datasets import mnist
from keras.models import Sequential
from keras.layers import Dense, Dropout, Flatten
from keras.layers import Conv2D, MaxPooling2D
import numpy as np

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batch_size = 128
num_classes = 10
epochs = 12

# input image dimensions
img_rows, img_cols = 28, 28

# the data, split between train and test sets
(x_train, y_train), (x_test, y_test) = mnist.load_data()

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model = Sequential()
model.add(Conv2D(32, kernel_size=(3, 3),
                 activation='relu',
                 input_shape=(28,28,1)))
model.add(Conv2D(64, (3, 3), activation='relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.25))
model.add(Flatten())
model.add(Dense(128, activation='relu'))
model.add(Dropout(0.5))