<jupyter_start><jupyter_text>Male Family Planning Analysis    <NAME>, Applied Data Science, Spring 2019<jupyter_code>import pandas as pd    
import numpy as np
import seaborn as sns
import statsmodels.formula.api as smf
from sklearn.linear_model import Lasso
from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestRegressor
import matplotlib.pyplot as plt

df = pd.read_stata('Raw_Data_Clipped.dta')
df.head()<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.06276150627615062, 'fraction_numerical': 0.008368200836820083, 'mean_word_length': 6.725806451612903, 'pattern_counts': {'":': 0, '<': 6, '<?xml version=': 0, '>': 6, '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': '503459', 'license': 'BSD-3-Clause', 'n_tokens_mistral': 163, 'n_tokens_neox': 154, 'n_words': 44, 'path': 'sophiasga/Atik_Proj2_Resub.ipynb', 'repo_name': 'em212/applied_ds', 'size': '483'}