Case Study Clothe Identification.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "fashion_mnist = pd.read_csv('Data/fashion-mnist_train.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(60000, 785)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fashion_mnist.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "img = fashion_mnist[1:2].values[:,1:].reshape(28,28)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x2045b76b4a8>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x20459a6f198>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(img, cmap='gray')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "feature_data = fashion_mnist.values[:,1:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "target_data = fashion_mnist.values[:,0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.svm import SVC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "svc = SVC(kernel='rbf', C=.1, gamma=100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "trainX, testX, trainY, testY = train_test_split(feature_data, target_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.pipeline import make_pipeline\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from sklearn.decomposition import PCA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "pipeline = make_pipeline(MinMaxScaler(), PCA(n_components=64), svc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import GridSearchCV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "params = {\n",
    "    'svc__C':[1,.1,.001,],\n",
    "    'svc__gamma':[10]\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "grid_model = GridSearchCV(pipeline, param_grid=params)"
   ]
  }
 ],
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   "language": "python",
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