Mo-Sam-Mo /
ML-Models
A comprehensive collection of machine learning model implementations, including supervised, unsupervised, and deep learning models, each accompanied by explanations, datasets, and Jupyter Notebook demonstrations.
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imehranasgari / repository
A comprehensive demonstration of Pandas and NumPy features for data selection, cleaning, transformation, and numerical analysis in Python. Includes hands-on Jupyter notebooks using the Titanic dataset, highlighting practical data science techniques for preprocessing and analysis.
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This project contains Jupyter notebooks demonstrating fundamental data manipulation and numerical computing techniques using Pandas and NumPy libraries in Python.
You can install the necessary libraries using pip or conda:
!conda install numpy
or
!pip install numpy
!pip install pandas
The project uses a titanic.csv dataset. Ensure this file is in the same directory as your Jupyter notebooks, or update the file path in the notebooks accordingly.
jupyter notebook
loc_iloc.ipynb or Pandas.ipynb or Numpy.ipynb) from the Jupyter interface in your web browser.loc_iloc.ipynb) pclass survived name sex age sibsp parch ticket fare cabin embarked boat body home.dest
1183 3 0 Salonen, Mr. Johan Werner male 39.0 0 0 3101296 7.9250 NaN S NaN NaN NaN
1101 3 0 Panula, Master. Eino Viljami male 1.0 4 1 3101295 39.6875 NaN S NaN NaN NaN
833 3 0 Green, Mr. George Henry male 51.0 0 0 21440 8.0500 NaN S NaN NaN Dorking, Surrey, England
584 2 1 Webber, Miss. Susan female 32.5 0 0 27267 13.0000 E101 S 12 NaN England / Hartford, CT
716 3 0 Chronopoulos, Mr. Apostolos male 26.0 1 0 2680 14.4542 NaN C NaN NaN Greece
Selected from shared topics, language and repository description—not editorial ratings.
Mo-Sam-Mo /
A comprehensive collection of machine learning model implementations, including supervised, unsupervised, and deep learning models, each accompanied by explanations, datasets, and Jupyter Notebook demonstrations.
69/100 healthmfurqaniftikhar /
A comprehensive educational collection of Jupyter notebooks demonstrating various generative models using TensorFlow/Keras. Learn from Bayesian sampling to GANs and VAEs through hands-on demonstrations.
49/100 healthdivya-09nimbalkar /
age):
0 29.00
1 0.92
2 2.00
3 30.00
4 25.00
...
1304 14.50
1305 NaN
1306 26.50
1307 27.00
1308 29.00
Name: age, Length: 1309, dtype: float64
age, pclass, fare):
age pclass fare
0 29.00 1 211.3375
1 0.92 1 151.5500
2 2.00 1 151.5500
3 30.00 1 151.5500
4 25.00 1 151.5500
... ... ... ...
1304 14.50 3 14.4542
1305 NaN 3 14.4542
1306 26.50 3 7.2250
1307 27.00 3 7.2250
1308 29.00 3 7.8750
[1309 rows x 3 columns]
iloc:
survived name parch
3 0 Allison, Mr. Hudson Joshua Creighton 2
4 0 Allison, Mrs. Hudson J C (Bessie Waldo Daniels) 2
iloc:
name sex age
9 Artagaveytia, Mr. Ramon male 71.0
10 Astor, Col. John Jacob male 47.0
11 Astor, Mrs. John Jacob (Madeleine Talmadge Force) female 18.0
12 Aubart, Mme. Leontine Pauline female 24.0
13 Barber, Miss. Ellen "Nellie" female 26.0
14 Barkworth, Mr. Algernon Henry Wilson male 80.0
15 Baumann, Mr. John D male NaN
16 Baxter, Mr. Quigg Edmond male 24.0
17 Baxter, Mrs. James (Helene DeLaudeniere Chaput) female 50.0
18 Bazzani, Miss. Albina female 32.0
19 Beattie, Mr. Thomson male 36.0
20 Beckwith, Mr. Richard Leonard male 37.0
21 Beckwith, Mrs. Richard Leonard (Sallie Monypeny) female 47.0
22 Behr, Mr. Karl Howell male 26.0
23 Bidois, Miss. Rosalie female 42.0
24 Bird, Miss. Ellen female 29.0
loc and boolean indexing:
pclass survived name sex age sibsp parch ticket fare cabin embarked boat body home.dest
0 1 1 Allen, Miss. Elisabeth Walton female 29.0 0 0 24160 211.3375 B5 S 2 NaN St Louis, MO
24 1 1 Bird, Miss. Ellen female 29.0 0 0 PC 17483 221.7792 C97 S 8 NaN NaN
111 1 1 Fortune, Miss. Alice Elizabeth female 24.0 3 2 19950 263.0000 C23 C25 C27 S 10 NaN Winnipeg, MB
112 1 1 Fortune, Miss. Ethel Flora female 28.0 3 2 19950 263.0000 C23 C25 C27 S 10 NaN Winnipeg, MB
113 1 1 Fortune, Miss. Mabel Helen female 23.0 3 2 19950 263.0000 C23 C25 C27 S 10 NaN Winnipeg, MB
116 1 1 Fortune, Mrs. Mark (Mary McDougald) female 60.0 1 4 19950 263.0000 C23 C25 C27 S 10 NaN Winnipeg, MB
180 1 1 Kreuchen, Miss. Emilie female 39.0 0 0 24160 211.3375 NaN S 2 NaN NaN
193 1 1 Madill, Miss. Georgette Alexandra female 15.0 0 1 24160 211.3375 B5 S 2 NaN St Louis, MO
238 1 1 Robert, Mrs. Edward Scott (Elisabeth Walton Mc... female 43.0 0 1 24160 211.3375 B3 S 2 NaN St Louis, MO
Pandas.ipynb).head()):
pclass survived name sex age sibsp parch ticket fare cabin embarked boat body home.dest
0 1 1 Allen, Miss. Elisabeth Walton female 29.00 0 0 24160 211.3375 B5 S 2 NaN St Louis, MO
1 1 1 Allison, Master. Hudson Trevor male 0.92 1 2 113781 151.5500 C22 C26 S 11 NaN Montreal, PQ / Chesterville, ON
2 1 0 Allison, Miss. Helen Loraine female 2.00 1 2 113781 151.5500 C22 C26 S NaN NaN Montreal, PQ / Chesterville, ON
3 1 0 Allison, Mr. Hudson Joshua Creighton male 30.00 1 2 113781 151.5500 C22 C26 S NaN 135.0 Montreal, PQ / Chesterville, ON
4 1 0 Allison, Mrs. Hudson J C (Bessie Waldo Daniels) female 25.00 1 2 113781 151.5500 C22 C26 S NaN NaN Montreal, PQ / Chesterville, ON
(1309, 14)
Index(['pclass', 'survived', 'name', 'sex', 'age', 'sibsp', 'parch', 'ticket',
'fare', 'cabin', 'embarked', 'boat', 'body', 'home.dest'],
dtype='object')
pclass int64
survived int64
name object
sex object
age float64
sibsp int64
parch int64
ticket object
fare float64
cabin object
embarked object
boat object
body float64
home.dest object
dtype: object
0 809
1 500
Name: survived, dtype: int64
0 61.802903
1 38.197097
Name: survived, dtype: float64
survived 0 1
sex
female 127 339
male 682 161
pclass survived name sex age sibsp parch ticket fare cabin embarked boat body home.dest
0 1 1 Allen, Miss. Elisabeth Walton female 29.0 0 0 24160 211.3375 B5 S 2 NaN St Louis, MO
342 2 1 Becker, Mrs. Allen Oliver (Nellie E Baumgardner) female 36.0 0 3 230136 39.0000 F4 S 11 NaN Guntur, India / Benton Harbour, MI
618 3 0 Allen, Mr. William Henry male 35.0 0 0 373450 8.0500 NaN S NaN NaN Lower Clapton, Middlesex or Erdington, Birmingham
array(['S', 'C', nan, 'Q'], dtype=object)
pclass
1 87.508992
2 21.179196
3 13.302889
Name: fare, dtype: float64
pclass age
0 1 39.159930
1 2 29.506705
2 3 24.816367
pclass survived name sex age sibsp parch ticket fare cabin embarked boat body home.dest
14 1 1 Barkworth, Mr. Algernon Henry Wilson male 80.0 0 0 27042 30.0000 A23 S B NaN Hessle, Yorks
61 1 1 Cavendish, Mrs. Tyrell William (Julia Florence... female 76.0 1 0 19877 78.8500 C46 S 6 NaN Little Onn Hall, Staffs
1235 3 0 Svensson, Mr. Johan male 74.0 0 0 347060 7.7750 NaN S NaN NaN NaN
135 1 0 Goldschmidt, Mr. George B male 71.0 0 0 PC 17754 34.6542 A5 C NaN NaN New York, NY
9 1 0 Artagaveytia, Mr. Ramon male 71.0 0 0 PC 17609 49.5042 NaN C NaN 22.0 Montevideo, Uruguay
... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
1293 3 0 Williams, Mr. Howard Hugh "Harry" male NaN 0 0 A/5 2466 8.0500 NaN S NaN NaN NaN
1297 3 0 Wiseman, Mr. Phillippe male NaN 0 0 A/4. 34244 7.2500 NaN S NaN NaN
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