grohith327

import tensorflow as tf
from tensorflow.keras.layers import Dense, Flatten, Conv2D, MaxPool2D, Dropout

# Create training and testing datasets from tensors
train_ds = tf.data.Dataset.from_tensor_slices((X_train, y_train)).batch(1)
test_ds = tf.data.Dataset.from_tensor_slices((X_test, y_test)).batch(1)

# CNN Model
class Command(Model):
    def __init__(self):

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import pandas as pd
import numpy as np

df_train = pd.read_csv('/Users/rohith/Documents/Datasets/Linear_Regression/train.csv')
df_test = pd.read_csv('/Users/rohith/Documents/Datasets/Linear_Regression/test.csv')

x_train = df_train['x']
y_train = df_train['y']
x_test = df_test['x']
y_test = df_test['y']

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from sklearn.svm import SVC
from sklearn.metrics import accuracy_score

clf = SVC(kernel='linear')
clf.fit(x_train,y_train)
y_pred = clf.predict(x_test)
print(accuracy_score(y_test,y_pred))

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import pyaudio
import wave
from keras.models import load_model
import librosa
import numpy as np
import warnings
import osascript
import webbrowser
import os
import cv2

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## Linear Regression 
import numpy as np

n = 700
alpha = 0.0001

a_0 = np.zeros((n,1))
a_1 = np.zeros((n,1))

epochs = 0

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import pandas as pd

df = pd.read_csv('/Users/rohith/Documents/Datasets/Iris_dataset/iris.csv') ## Load data
df = df.drop(['Id'],axis=1)
rows = list(range(100,150))
df = df.drop(df.index[rows])  ## Drop the rows with target values Iris-virginica
Y = []
target = df['Species']
for val in target:
    if(val == 'Iris-setosa'):

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import matplotlib.pyplot as plt 

y_prediction = a_0 + a_1 * x_test
print('R2 Score:',r2_score(y_test,y_prediction))

y_plot = []
for i in range(100):
    y_plot.append(a_0 + a_1 * i)
plt.figure(figsize=(10,10))
plt.scatter(x_test,y_test,color='red',label='GT')

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import pyaudio
import wave
import warnings


warnings.filterwarnings(action='ignore',category=FutureWarning)

CHUNK = 256
FORMAT = pyaudio.paInt16
CHANNELS = 2

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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 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])