<jupyter_start><jupyter_text>print(median_test)<jupyter_code>print(median_test)
df.test_s = df.test_s.fillna(median_test)
df
df
df.experience = df.experience.fillna(0)
df
df
for index, row in df.iterrows():
   print (index)
df['experience'] 
df.experience
df.experience = w2n.word_to_num(df.experience)<jupyter_output><empty_output><jupyter_text>df<jupyter_code>df
for index, row in df.iterrows():
    row[index] = w2n.word_to_num(row['experience'])
w2n.word_to_num(df.experience[1])
for index, row in df.iterrows():
    df.experience[index] = w2n.word_to_num(df.experience[index])
for x in range(2, 8):
    df.experience[x] = w2n.word_to_num(df.experience[x])
df
reg = linear_model.LinearRegression()
reg.fit(df[['experience','test_s','interview_s']],df.salary)
reg.coef_
reg.intercept_
reg.predict([[2, 9, 6]])
reg.predict([[12, 10, 10]])
df.test_s[6] = 7
df
df.salary
df.salary.iloc[x:x+2]
x=2
x
df[['experience','test_s','interview_s']].iloc[x:x+4]
reg.fit(df[['experience','test_s','interview_s']].iloc[x:x+5],df.salary.iloc[x:x+5])
reg.coef_, reg.intercept_
reg.get_params
df<jupyter_output><empty_output>