ML-For-Beginners/translations/sw/4-Classification/2-Classifiers-1

Vihesabu vya Chakula 1

Katika somo hili, utatumia dataset uliyohifadhi kutoka somo lililopita lililojaa data safi na iliyosawazishwa kuhusu aina za vyakula.

Utatumia dataset hii na aina mbalimbali za vihesabu (classifiers) kutabiri aina ya chakula cha kitaifa kulingana na kikundi cha viungo. Wakati wa kufanya hivyo, utajifunza zaidi kuhusu baadhi ya njia ambazo algorithimu zinaweza kutumika kwa kazi za uainishaji.

Pre-lecture quiz

Maandalizi

Kama umehitimisha Somo la 1, hakikisha kuwa faili cleaned_cuisines.csv ipo katika folda ya mizizi /data kwa ajili ya masomo haya manne.

Zoezi - tabiri aina ya chakula cha kitaifa

1. Ukifanya kazi katika folda ya notebook.ipynb ya somo hili, leta faili hilo pamoja na maktaba ya Pandas:

   import pandas as pd
   cuisines_df = pd.read_csv("../data/cleaned_cuisines.csv")
   cuisines_df.head()
   

   Data inaonekana hivi:

	Unnamed: 0	cuisine	almond	angelica	anise	anise_seed	apple	apple_brandy	apricot	armagnac	...	whiskey	white_bread	white_wine	whole_grain_wheat_flour	wine	wood	yam	yeast	yogurt	zucchini
0	0	indian	0	0	0	0	0	0	0	0	...	0	0	0	0	0	0	0	0	0	0
1	1	indian	1	0	0	0	0	0	0	0	...	0	0	0	0	0	0	0	0	0	0
2	2	indian	0	0	0	0	0	0	0	0	...	0	0	0	0	0	0	0	0	0	0
3	3	indian	0	0	0	0	0	0	0	0	...	0	0	0	0	0	0	0	0	0	0
4	4	indian	0	0	0	0	0	0	0	0	...	0	0	0	0	0	0	0	0	1	0

1. Sasa, leta maktaba zaidi kadhaa:

   from sklearn.linear_model import LogisticRegression
   from sklearn.model_selection import train_test_split, cross_val_score
   from sklearn.metrics import accuracy_score,precision_score,confusion_matrix,classification_report, precision_recall_curve
   from sklearn.svm import SVC
   import numpy as np
   

2. Gawanya viwianishi vya X na y katika dataframes mbili kwa mafunzo. cuisine inaweza kuwa dataframe ya lebo:

   cuisines_label_df = cuisines_df['cuisine']
   cuisines_label_df.head()
   

   Itaonekana hivi:

   0    indian
   1    indian
   2    indian
   3    indian
   4    indian
   Name: cuisine, dtype: object
   

3. Ondoa Unnamed: 0 column and the cuisine column, calling drop(). Hifadhi data iliyobaki kama sifa za kufundisha:

   cuisines_feature_df = cuisines_df.drop(['Unnamed: 0', 'cuisine'], axis=1)
   cuisines_feature_df.head()
   

   Sifa zako zinaonekana hivi:

	almond	angelica	anise	anise_seed	apple	apple_brandy	apricot	armagnac	artemisia	artichoke	...	whiskey	white_bread	white_wine	whole_grain_wheat_flour	wine	wood	yam	yeast	yogurt	zucchini
0	0	0	0	0	0	0	0	0	0	0	...	0	0	0	0	0	0	0	0	0	0
1	1	0	0	0	0	0	0	0	0	0	...	0	0	0	0	0	0	0	0	0	0
2	0	0	0	0	0	0	0	0	0	0	...	0	0

Onyo: Hati hii imetafsiriwa kwa kutumia huduma za tafsiri za AI zinazotumia mashine. Ingawa tunajitahidi kwa usahihi, tafadhali fahamu kuwa tafsiri za kiotomatiki zinaweza kuwa na makosa au kutokubaliana. Hati ya asili katika lugha yake ya asili inapaswa kuzingatiwa kama chanzo cha mamlaka. Kwa taarifa muhimu, tafsiri ya kitaalamu ya kibinadamu inapendekezwa. Hatutawajibika kwa kutoelewana au tafsiri zisizo sahihi zinazotokana na matumizi ya tafsiri hii.