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Course Notes: Intermediate Python

Course Notes

Use this workspace to take notes, store code snippets, or build your own interactive cheatsheet! The datasets used in this course are available in the datasets folder.

# Import any packages you want to use here
import pandas as pd

Add your notes here

Take Notes

Add notes here about the concepts you've learned and code cells with code you want to keep.

# Add your code snippets here
Spinner
DataFrameavailable as
df
variable
SELECT "tweet", "class" FROM 'hatespeechlabel_data.csv'
df.describe()

import pandas as pd
import numpy as np
import tensorflow as tf
from tensorflow.keras.preprocessing.text import Tokenizer
from tensorflow.keras.preprocessing.sequence import pad_sequences
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Embedding, Conv1D, GlobalMaxPooling1D, Dense

# Assuming your dataframe has a 'text' column
df = pd.read_csv('hatespeechlabel_data.csv')


hate_s = df[["tweet","class"]]
hate_s.head().describe ()

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.model_selection import train_test_split
from sklearn.model_selection import cross_val_score
from keras.models import Sequential
from keras.layers import Dense, BatchNormalization, Dropout
from keras.optimizers import Adam, SGD, RMSprop, Adadelta, Adagrad, Adamax, Nadam, Ftrl
from keras.callbacks import EarlyStopping, ModelCheckpoint
from keras.wrappers.scikit_learn import KerasClassifier
from math import floor
from sklearn.metrics import make_scorer, accuracy_score
from sklearn.model_selection import StratifiedKFold
from keras.layers import LeakyReLU
import warnings
warnings.filterwarnings('ignore')
pd.set_option("display.max_columns", None)

!pip install bayesian-optimization
from bayes_opt import BayesianOptimization