import pandas as pd
import numpy as np
df = pd.read_csv('data.csv')
df.dropna(inplace=True)
from sklearn.linear_model import LinearRegression
model = LinearRegression()
model.fit(X_train, y_train)
preds = model.predict(X_test)
import tensorflow as tf
model = tf.keras.Sequential()
model.add(Dense(128, activation='relu'))
model.compile(optimizer='adam', loss='mse')
import matplotlib.pyplot as plt
plt.plot(data)
plt.title('Loss Curve')
plt.show()
from sklearn.cluster import KMeans
kmeans = KMeans(n_clusters=3)
kmeans.fit(X)
labels = kmeans.labels_
import torch
import torch.nn as nn
class Net(nn.Module):
def __init__(self):
super().__init__()
from transformers import pipeline
nlp = pipeline('sentiment-analysis')
result = nlp('I love AI!')
print(result)
import seaborn as sns
sns.heatmap(df.corr())
plt.title('Correlation Matrix')
plt.show()
from sklearn.model_selection import
train_test_split
X_train, X_test, y_train, y_test =
train_test_split(X, y, test_size=0.2)
accuracy = model.evaluate(X_test, y_test)
print(f'Accuracy: {accuracy[1]*100:.2f}%')
model.save('model.h5')
from sklearn.preprocessing import
StandardScaler
scaler = StandardScaler()
X_scaled = scaler.fit_transform(X)
import cv2
img = cv2.imread('image.jpg')
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
cv2.imshow('Gray', gray)
from sklearn.metrics import
confusion_matrix, classification_report
print(classification_report(y_test, preds))
import nltk
from nltk.tokenize import word_tokenize
tokens = word_tokenize(text)
print(tokens)
from scipy import stats
z_scores = stats.zscore(df)
df_clean = df[(z_scores < 3).all(axis=1)]
print(df_clean.shape)
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