In [6]:
!pip install pandas-profiling
Collecting pandas-profiling
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Building wheels for collected packages: confuse, htmlmin, pyyaml, imagehash
Building wheel for confuse (setup.py) ... done
Created wheel for confuse: filename=confuse-1.1.0-py3-none-any.whl size=17573 sha256=c75ad1ae731e6e17e670156bbc556c84e54278f025205c6a10e373178dd7334e
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Created wheel for htmlmin: filename=htmlmin-0.1.12-py3-none-any.whl size=27084 sha256=1692781c041008f2687d2ec2d50d28b96f6d6ed9c363d3db36b92119d090ad1c
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Created wheel for pyyaml: filename=PyYAML-5.3.1-cp37-cp37m-linux_x86_64.whl size=393721 sha256=e18408326b01bd797c00313150ddd6be9b0b6b0e6d755bde8103761fb8b37362
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Stored in directory: /home/jovyan/.cache/pip/wheels/16/72/b0/e5be34699908d9ff25dcb3debbb717987b766af61bbddffdfe
Successfully built confuse htmlmin pyyaml imagehash
Installing collected packages: missingno, llvmlite, numba, joblib, phik, tqdm, pyyaml, confuse, astropy, htmlmin, networkx, tangled-up-in-unicode, Pillow, PyWavelets, imagehash, visions, pandas-profiling
Successfully installed Pillow-7.1.2 PyWavelets-1.1.1 astropy-4.0.1.post1 confuse-1.1.0 htmlmin-0.1.12 imagehash-4.1.0 joblib-0.15.1 llvmlite-0.33.0 missingno-0.4.2 networkx-2.4 numba-0.50.0 pandas-profiling-2.8.0 phik-0.10.0 pyyaml-5.3.1 tangled-up-in-unicode-0.0.6 tqdm-4.46.1 visions-0.4.4
In [7]:
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
%matplotlib inline
In [8]:
import seaborn as sns
import pandas_profiling
In [9]:
path ="https://raw.githubusercontent.com/reddyprasade/Data-Sets-For-Machine-Learnig-and-Data-Science/master/DataSets/height-weight.csv"
In [12]:
df = pd.read_csv(path,names=['Height',"Weight"],)
In [13]:
df.head()
Out[13]:
| Height | Weight | |
|---|---|---|
| 0 | 73.847017 | 241.893563 |
| 1 | 68.781904 | 162.310473 |
| 2 | 74.110105 | 212.740856 |
| 3 | 71.730978 | 220.042470 |
| 4 | 69.881796 | 206.349801 |
In [14]:
df.tail()
Out[14]:
| Height | Weight | |
|---|---|---|
| 4995 | 68.860062 | 177.131052 |
| 4996 | 68.973423 | 159.285228 |
| 4997 | 67.013795 | 199.195400 |
| 4998 | 71.557718 | 185.905910 |
| 4999 | 70.351880 | 198.903012 |
In [15]:
df.info()
<class 'pandas.core.frame.DataFrame'> RangeIndex: 5000 entries, 0 to 4999 Data columns (total 2 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 Height 5000 non-null float64 1 Weight 5000 non-null float64 dtypes: float64(2) memory usage: 78.2 KB
In [16]:
df.describe()
Out[16]:
| Height | Weight | |
|---|---|---|
| count | 5000.000000 | 5000.000000 |
| mean | 69.026346 | 187.020621 |
| std | 2.863362 | 19.781155 |
| min | 58.406905 | 112.902939 |
| 25% | 67.174679 | 173.887767 |
| 50% | 69.027709 | 187.033546 |
| 75% | 70.988744 | 200.357802 |
| max | 78.998742 | 269.989698 |
In [20]:
sns.pairplot(df,diag_kind="kde")
Out[20]:
<seaborn.axisgrid.PairGrid at 0x7f419844e810>
In [21]:
pandas_profiling.ProfileReport(df)
HBox(children=(FloatProgress(value=0.0, description='Summarize dataset', max=16.0, style=ProgressStyle(descrip…
HBox(children=(FloatProgress(value=0.0, description='Generate report structure', max=1.0, style=ProgressStyle(…
HBox(children=(FloatProgress(value=0.0, description='Render HTML', max=1.0, style=ProgressStyle(description_wi…
Out[21]:
In [22]:
!pip install scikit-learn
Collecting scikit-learn
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Downloading threadpoolctl-2.1.0-py3-none-any.whl (12 kB)
Installing collected packages: threadpoolctl, scikit-learn
Successfully installed scikit-learn-0.23.1 threadpoolctl-2.1.0
In [24]:
from sklearn.linear_model import LinearRegression
In [26]:
Feature = df[['Height']]
In [28]:
Target = df.Weight
In [47]:
plt.figure(figsize=(30,16))
plt.scatter(Feature,Target)
Out[47]:
<matplotlib.collections.PathCollection at 0x7f4194815410>
In [25]:
Lr = LinearRegression()
In [29]:
Lr.fit(Feature,Target)
Out[29]:
LinearRegression()
In [31]:
Lr.coef_ ## This Slope of Line Equation
Out[31]:
array([5.96177381])
In [33]:
Lr.intercept_
Out[33]:
-224.49884070650657
In [34]:
Lr.normalize
Out[34]:
False
In [36]:
yhat=Lr.predict([[70.351880]])
In [38]:
print("if Height is 70.351880 then Weight is {}".format(yhat))
if Height is 70.351880 then Weight is [194.92315511]
In [43]:
actual = df.Weight.iloc[-1]
actual
Out[43]:
198.9030119
In [44]:
error = actual-yhat
In [45]:
error
Out[45]:
array([3.97985679])
$yhat$ = $\beta_0 + \beta_1*X $
In [51]:
plt.figure(figsize=(30,16))
plt.scatter(Feature,Target)
plt.plot(Feature,Lr.intercept_+ Lr.coef_*Feature,'y-')
plt.title("Simple Linear Regression")
Out[51]:
Text(0.5, 1.0, 'Simple Linear Regression')
In [ ]: