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[lab_classification] Vicente Díaz Pliego #1

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340 changes: 340 additions & 0 deletions .ipynb_checkpoints/Supervised Classification-checkpoint.ipynb
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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Supervised classification\n",
"\n",
"In the data.csv there are letters (uppercases and lowercases) and numbers, 28x28 pixels in a row format.\n",
"\n",
"* First, you need to know which labels are which, meaning you need to visualize some data to realize which number labels represents a letter, or a number."
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Comenzando...\n",
"\n",
"Modelo SoftMax (MLR) : Numeros\n"
]
}
],
"source": [
"# Import libraries\n",
"import zipfile\n",
"zf=zipfile.ZipFile('data_all.zip')\n",
"import time # para tiempo\n",
"inicio=time.time() # momento inicial\n",
"print ('Comenzando...\\n')\n",
"print ('Modelo SoftMax (MLR) : Numeros') \n",
"import pandas as pd # dataframe\n",
"import numpy as np # numerical python, algebra lineal\n",
"\n",
"import matplotlib.pyplot as plt # plots, graficos\n",
"import seaborn as sns # plots\n",
"%matplotlib inline\n",
"\n",
"from sklearn.metrics import confusion_matrix # metricas, matriz de confusion\n",
"from scipy.optimize import minimize # minimizar, opt\n",
"\n",
"from sklearn.datasets import make_circles, load_boston # datasets\n",
"from sklearn.model_selection import train_test_split as tts\n",
"\n",
"from sklearn.model_selection import GridSearchCV\n",
"from sklearn.model_selection import RandomizedSearchCV\n",
"\n",
"import warnings # avisos\n",
"warnings.filterwarnings('ignore', category=RuntimeWarning)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
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},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df=pd.read_csv(zf.open('data_all.csv'), nrows=1e5)\n",
"\n",
"df.head()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Your code here"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"* Now, try to train a classifier model to predict the uppercases. Use every single model you know for classification."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Your code here"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"* Try to do the same thing with lowercases."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Your code here"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"* Try to do the same thing with numbers."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Your code here"
]
}
],
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