<jupyter_start><jupyter_text>EXPLORATORY DATA ANALYSIS<jupyter_code># Import all required modules
import datetime as dt 
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
%matplotlib inline

# For training
from sklearn import linear_model
from sklearn.model_selection import train_test_split

# For evaluation
from sklearn.metrics import mean_squared_error, mean_absolute_error
from sklearn.metrics import r2_score
finances = pd.read_csv('diaries_transactions_all.csv')

finances.shape
# columns
finances.columns
finances.describe()
finances.head()
finances.tail()
finances['int_year'].value_counts(ascending=True)
finances.groupby('int_year').size().plot();<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.07934336525307797, 'fraction_numerical': 0.0013679890560875513, 'mean_word_length': 7.819277108433735, 'pattern_counts': {'":': 0, '<': 5, '<?xml version=': 0, '>': 5, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'chain_length': '1', 'dir': 'jupyter-structured-clean-dedup', 'id': '402758', 'license': 'MIT', 'n_tokens_mistral': 242, 'n_tokens_neox': 237, 'n_words': 60, 'path': 'Tabitha.ipynb', 'repo_name': 'Kavyu/kamilimu', 'size': '731'}