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Hepatitis_C_Prediction
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Files changed (13) hide show
  1. HepatitisCdata.csv +616 -0
  2. app.py +10 -0
  3. cat_columns.txt +1 -0
  4. eda.py +88 -0
  5. enc_columns.txt +1 -0
  6. hepatitis.jpg +0 -0
  7. main.py +10 -0
  8. norm_columns.txt +1 -0
  9. num_columns.txt +1 -0
  10. pipelines.pkl +3 -0
  11. prediction.py +76 -0
  12. requirements.txt +9 -0
  13. skew_columns.txt +1 -0
HepatitisCdata.csv ADDED
@@ -0,0 +1,616 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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135
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136
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137
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138
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139
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140
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141
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142
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143
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144
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145
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146
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147
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148
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149
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150
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151
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152
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153
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154
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155
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156
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157
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158
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159
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160
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161
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162
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163
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164
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165
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166
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167
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168
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169
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170
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171
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172
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173
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174
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175
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176
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177
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178
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179
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180
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181
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182
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183
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184
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185
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186
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187
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188
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189
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190
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191
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192
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193
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194
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195
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196
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197
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198
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199
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200
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201
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202
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203
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204
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205
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206
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207
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208
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209
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210
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212
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213
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214
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215
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216
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217
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218
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219
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220
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221
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222
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223
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224
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225
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226
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227
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228
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229
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230
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231
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232
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233
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234
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235
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236
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237
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238
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239
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240
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241
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242
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243
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244
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245
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247
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248
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249
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250
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251
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252
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253
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254
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255
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256
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257
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258
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259
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260
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261
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262
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263
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264
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265
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266
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267
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268
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269
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270
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271
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272
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273
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274
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275
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276
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277
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278
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279
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280
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281
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282
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283
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284
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285
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286
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287
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288
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289
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290
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291
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292
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293
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294
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295
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296
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297
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298
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299
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300
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301
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302
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303
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304
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305
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306
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307
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308
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309
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310
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311
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312
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313
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314
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315
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316
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317
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318
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319
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320
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321
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322
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323
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324
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325
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326
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327
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328
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329
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330
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331
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332
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333
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334
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335
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336
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337
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338
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339
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340
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341
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342
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343
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344
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345
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346
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347
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348
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349
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350
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351
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352
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353
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354
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355
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356
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357
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358
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359
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360
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361
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362
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363
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364
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365
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366
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367
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368
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369
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370
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371
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372
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373
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374
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375
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376
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377
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378
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379
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380
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381
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382
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383
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384
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385
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386
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387
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388
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389
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390
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391
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392
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393
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394
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395
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396
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397
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398
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399
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400
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401
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402
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403
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404
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405
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406
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407
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408
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409
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410
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411
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412
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413
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414
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415
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416
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417
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418
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419
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420
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421
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422
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423
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424
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425
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426
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427
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428
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429
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430
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431
+ "430","0=Blood Donor",48,"f",45.3,40.6,18.5,27.7,5.7,7.48,4.64,66,19.6,70.6
432
+ "431","0=Blood Donor",48,"f",37.5,88.3,14.7,19.6,5.4,9.28,4.68,61,12.3,67.9
433
+ "432","0=Blood Donor",48,"f",39.6,65.9,64.3,39.5,2.8,8.8,6.14,77,24.1,75.8
434
+ "433","0=Blood Donor",48,"f",43.7,50.1,17.3,26.3,8.1,8.15,5.38,64,13.4,73.1
435
+ "434","0=Blood Donor",48,"f",46.8,93.3,10,23.2,4.3,12.41,NA,52,23.9,72.4
436
+ "435","0=Blood Donor",48,"f",42.5,62.2,12.1,20.1,23.1,4.01,5.58,67,13,74.2
437
+ "436","0=Blood Donor",48,"f",44.4,52.5,16.4,23.4,4.5,9.06,6.78,74,10.3,73.1
438
+ "437","0=Blood Donor",48,"f",44.4,64.5,17.2,21.1,17.3,4.93,4.05,67,12.1,71.2
439
+ "438","0=Blood Donor",48,"f",38.8,43.9,12.8,13.3,8.6,5.63,5.31,66,21.4,63.2
440
+ "439","0=Blood Donor",49,"f",38.8,120.2,25.2,21.5,12,8.29,7.11,52,18.6,70.7
441
+ "440","0=Blood Donor",49,"f",40.9,58.4,20.1,26.5,2.6,9.77,5.99,53,12.7,73.9
442
+ "441","0=Blood Donor",49,"f",39.1,89.4,15.4,24.1,4.1,10.03,8.36,74,12,68.1
443
+ "442","0=Blood Donor",49,"f",39.3,59.4,18.3,15,4.8,8.03,4.58,83,12.5,74.3
444
+ "443","0=Blood Donor",49,"f",40.5,31.3,16.2,19.4,11.2,4.95,5.1,75,14.9,73.2
445
+ "444","0=Blood Donor",49,"f",34.9,37.9,15.3,19.4,7.1,5.3,5.88,83,7.9,62.5
446
+ "445","0=Blood Donor",49,"f",45.4,45.9,14.3,15.9,5.8,9.05,6.81,69,14.5,78.2
447
+ "446","0=Blood Donor",49,"f",43.3,71.5,28.4,26,6.2,7.68,5.91,77,19.1,76.9
448
+ "447","0=Blood Donor",50,"f",36.9,50.6,15.5,21.7,2.1,9.97,7.01,68,17.6,68.4
449
+ "448","0=Blood Donor",50,"f",47.6,77.2,24,17.5,4.8,9.27,6.41,77,21.3,73.5
450
+ "449","0=Blood Donor",50,"f",42.5,74,10.9,23,4.6,9.42,6.33,76,9,68.8
451
+ "450","0=Blood Donor",50,"f",39.9,80.5,24.2,22.8,5.2,9.25,7.41,84,19.4,71.2
452
+ "451","0=Blood Donor",50,"f",41.2,45.6,25,25.1,4.1,7.16,6.54,64,15.4,68.2
453
+ "452","0=Blood Donor",50,"f",34.6,63,24.7,31,12.8,6.55,5.95,70,25.4,70.2
454
+ "453","0=Blood Donor",51,"f",46.3,70.5,75.2,69.2,6.1,7.68,4.73,69,52.2,78.3
455
+ "454","0=Blood Donor",51,"f",37.8,80.7,32.9,27.3,6.2,7.29,5.26,68,27.1,70.6
456
+ "455","0=Blood Donor",51,"f",38.3,52.9,12.4,16.5,3.8,7.22,5.43,55,12.7,70.2
457
+ "456","0=Blood Donor",51,"f",39.6,63.5,17,25.9,7.3,7,6.97,72,10.4,71.9
458
+ "457","0=Blood Donor",51,"f",38.3,67.7,17.4,19.9,5.4,7.5,4.94,72,7.9,69
459
+ "458","0=Blood Donor",51,"f",43.1,52.2,18.5,24.1,8.1,7.52,6.76,60,17.4,71.9
460
+ "459","0=Blood Donor",51,"f",41.4,136.9,33.2,20,5,10.27,6.24,77,106.7,72.2
461
+ "460","0=Blood Donor",51,"f",41,81.2,35.1,33.3,5.2,8.9,6.56,70,21.7,72.4
462
+ "461","0=Blood Donor",51,"f",46,71,18.1,23.8,59.1,5.99,3.25,82,11.4,73.3
463
+ "462","0=Blood Donor",51,"f",39.6,43,16.3,19.6,7.5,5.57,4.96,83,62.1,72.9
464
+ "463","0=Blood Donor",51,"f",47.4,117.3,62.1,30.4,3.8,10.43,6.59,86,69.3,71
465
+ "464","0=Blood Donor",51,"f",43.7,61.3,18,23.3,4.3,9.57,6.04,70,18.6,75.2
466
+ "465","0=Blood Donor",52,"f",45.5,78.2,25.2,27.7,4.7,8.65,6.04,65,28.3,70.5
467
+ "466","0=Blood Donor",52,"f",43.8,52,15.5,23.9,6,7.93,5.41,69,11.9,72.4
468
+ "467","0=Blood Donor",52,"f",36,47.2,19.6,22.5,5.9,7.85,5.69,85,30.4,69.2
469
+ "468","0=Blood Donor",52,"f",41.7,58.3,22.9,26.7,11.4,9.17,4.33,75,14.5,77.1
470
+ "469","0=Blood Donor",52,"f",40.2,89.7,26.8,19.1,6.9,9.1,7.04,75,41.6,67.4
471
+ "470","0=Blood Donor",52,"f",51.5,81.8,26.3,20.6,7.8,6.74,5.9,88,16.3,82.2
472
+ "471","0=Blood Donor",52,"f",36.7,87.6,34.3,30.8,17.7,10.12,6.98,72,24.2,66.3
473
+ "472","0=Blood Donor",52,"f",41.3,77.4,16.6,22.2,5,7.57,7.8,66,10.8,70
474
+ "473","0=Blood Donor",52,"f",38.2,70.3,19.5,17.5,2.7,10.02,6.17,65,35.5,71.1
475
+ "474","0=Blood Donor",53,"f",42.4,55,20.9,42.4,7.7,6.6,4.26,67,14.2,70.9
476
+ "475","0=Blood Donor",53,"f",39,76,25.9,20.7,2.8,11.11,6.38,66,50.1,70.8
477
+ "476","0=Blood Donor",53,"f",40.1,84.6,23,22.1,7.1,8.4,5.16,70,82.6,74.6
478
+ "477","0=Blood Donor",53,"f",43.7,84.3,18.8,18.6,8.5,10.22,6.65,56,16.5,76.9
479
+ "478","0=Blood Donor",53,"f",43.5,61.7,16.9,20.3,7,7.19,6.97,74,12.3,69.2
480
+ "479","0=Blood Donor",53,"f",41.1,91.7,13.8,19.6,3.4,7.87,5.48,72,77.3,77.3
481
+ "480","0=Blood Donor",53,"f",38,84.7,23.5,19.8,10.8,7.3,4.82,62,11.4,68.5
482
+ "481","0=Blood Donor",53,"f",47.4,66.9,24.3,32.6,14.9,10.51,6.03,81,25.2,75
483
+ "482","0=Blood Donor",53,"f",51.3,84.1,40.6,43.6,9.2,7.1,5.62,62,74.9,77.1
484
+ "483","0=Blood Donor",54,"f",39.9,30.7,17,19.3,6.3,6.99,4.95,68,13.3,70.7
485
+ "484","0=Blood Donor",54,"f",26.2,72.9,28.5,28.8,5.5,7.49,4.91,58,27.6,57
486
+ "485","0=Blood Donor",54,"f",44.5,53.4,13.4,17.5,5.4,7.74,5.55,66,15.4,71.9
487
+ "486","0=Blood Donor",54,"f",39.9,61.2,23.3,24.5,4.5,9.22,5.47,69,58.2,65.8
488
+ "487","0=Blood Donor",54,"f",43.3,76.5,19.9,22.4,7,8.04,6.77,67,17.7,73.3
489
+ "488","0=Blood Donor",55,"f",41.9,86.1,19.3,22.3,7,8.21,5.05,71,32.5,75.2
490
+ "489","0=Blood Donor",55,"f",36.2,101.3,19.2,21.9,4.9,6.5,5.86,66,12.3,70.3
491
+ "490","0=Blood Donor",55,"f",35.2,69.6,23.6,26,3.8,4.97,4.43,69,29.4,60.5
492
+ "491","0=Blood Donor",55,"f",39.9,83.6,18.4,27.4,8,8.43,7.67,73,13.6,73.3
493
+ "492","0=Blood Donor",56,"f",39.7,66,14.2,20.8,3.5,7.48,5.88,66,7.2,67.2
494
+ "493","0=Blood Donor",56,"f",39.5,86.9,22.5,22.2,4.7,9.4,6.02,66,13,72.6
495
+ "494","0=Blood Donor",56,"f",34.7,90.3,22.7,21.6,3.5,8.07,5.45,67,9,69.4
496
+ "495","0=Blood Donor",56,"f",36.6,102.3,13.5,14.9,8.4,6.94,5.5,65,16.2,71
497
+ "496","0=Blood Donor",56,"f",33.2,54.3,15.5,22.8,8,5.61,3.87,55,19.1,63.1
498
+ "497","0=Blood Donor",56,"f",45.1,79.1,39,30.5,5.2,6.47,5.1,64,145.3,66.7
499
+ "498","0=Blood Donor",56,"f",39.9,83.8,19.1,23.6,4.3,7.61,6.06,72,16.4,67.1
500
+ "499","0=Blood Donor",57,"f",48.4,94.4,2.5,39.6,2.3,8.84,NA,82,6.4,76.8
501
+ "500","0=Blood Donor",57,"f",41.2,83.5,32.6,39.3,4,10.67,8.46,69,22.4,75.7
502
+ "501","0=Blood Donor",57,"f",42.6,57.1,15,18.9,5.3,8.9,5.93,61,21.3,74.8
503
+ "502","0=Blood Donor",57,"f",38.6,80.9,33.1,26.7,6.5,6.45,5.1,59,11.3,70.9
504
+ "503","0=Blood Donor",57,"f",27.3,85.1,18.4,25.4,2.2,8.96,6.66,68,10.2,62.5
505
+ "504","0=Blood Donor",57,"f",37.9,50.3,12.2,18.1,3.5,6.72,5.06,71,10,69.3
506
+ "505","0=Blood Donor",57,"f",38.7,62.8,21.8,29.2,9.2,6.55,7.08,68,13,70.7
507
+ "506","0=Blood Donor",57,"f",48,56.9,8.6,20.1,4.4,8.14,6.9,63,14.8,73.1
508
+ "507","0=Blood Donor",57,"f",44.7,60.6,16.5,24.3,4.2,10.47,4.9,68,15.9,68.5
509
+ "508","0=Blood Donor",57,"f",42.7,62.9,11.2,16.6,11.6,12.7,6.25,62,8.1,71.9
510
+ "509","0=Blood Donor",58,"f",35.3,91.4,14.7,21,18,6.58,5.51,60,10.2,70.4
511
+ "510","0=Blood Donor",58,"f",26.3,52.5,39.5,77.2,5.8,5.15,3.53,40,31.2,51
512
+ "511","0=Blood Donor",58,"f",47,74.8,36.1,28.9,5.3,9.82,6.71,70,32,76.6
513
+ "512","0=Blood Donor",59,"f",43,82.4,33.1,30,7.5,9.2,7.43,61,30.7,75.4
514
+ "513","0=Blood Donor",59,"f",45.1,78,26,32.4,9,9.85,7.32,70,12.9,70.9
515
+ "514","0=Blood Donor",59,"f",40,68.4,13.2,20.3,8.2,9.1,6.38,63,16.3,71.9
516
+ "515","0=Blood Donor",60,"f",40.5,86,26,27.6,5.7,8.6,4.9,58,20.1,71.7
517
+ "516","0=Blood Donor",60,"f",35.8,75.5,28.7,21.5,2,9.05,4.56,70,24,71.8
518
+ "517","0=Blood Donor",60,"f",52.4,88.2,50.2,31.7,8.8,8.49,5,77,47,77
519
+ "518","0=Blood Donor",60,"f",40.1,80.7,34.6,31.2,11.9,9.32,6.94,68,27.4,76.6
520
+ "519","0=Blood Donor",60,"f",45.4,51.4,16.4,19,6.8,10.78,6.79,78,12.5,72.8
521
+ "520","0=Blood Donor",60,"f",41.4,85.1,15.6,18.6,10,8.31,5.34,103,13,71.5
522
+ "521","0=Blood Donor",61,"f",38.1,66.4,11.8,22.7,6.8,6.35,6.55,65,14.5,78.9
523
+ "522","0=Blood Donor",61,"f",43.4,47.5,12.8,17.3,5.6,7.52,5.81,71,11.8,69.9
524
+ "523","0=Blood Donor",62,"f",36.6,82.5,18.2,24.3,10.6,9.82,7.12,54,15.6,67.8
525
+ "524","0=Blood Donor",62,"f",35.4,59.7,21.2,24.7,3.3,9,7.45,59,17.7,65.4
526
+ "525","0=Blood Donor",62,"f",44,46.6,24.8,25.5,7.6,6.75,4.59,91,39.5,74.3
527
+ "526","0=Blood Donor",63,"f",47.6,59.9,21.1,27.8,8.4,9.24,6.08,62,18.3,74.5
528
+ "527","0=Blood Donor",63,"f",36.1,67.6,13.8,12.2,5.8,9.44,6.88,66,22.4,65
529
+ "528","0=Blood Donor",63,"f",27.8,85.7,25.4,38.9,4.2,6.06,3.96,63,46,56.9
530
+ "529","0=Blood Donor",64,"f",39.8,68.1,20.5,24.8,5.8,7.53,7.58,56,14.2,65.8
531
+ "530","0=Blood Donor",64,"f",43,52.7,31.6,29.2,5.5,11.69,6,63,61.9,73.4
532
+ "531","0=Blood Donor",65,"f",33,74.3,13.2,16.8,4.1,7.46,5.76,52,9.1,64.2
533
+ "532","0=Blood Donor",68,"f",41.4,102.3,38.4,26.4,6.8,8.5,6.79,59,23.8,68.9
534
+ "533","0=Blood Donor",70,"f",40,97.9,15.1,15.9,6.8,11.46,5.08,62,19.2,65.3
535
+ "534","0s=suspect Blood Donor",47,"m",22.5,124,79.5,46.7,2.3,6.83,4.3,170,345.6,58.6
536
+ "535","0s=suspect Blood Donor",48,"m",24.9,116.9,49.2,24.3,4.9,3.44,5.25,29,83,47.8
537
+ "536","0s=suspect Blood Donor",49,"m",21.6,42.2,9.5,10.6,2.4,3.75,3.01,64,38.9,44.8
538
+ "537","0s=suspect Blood Donor",55,"m",47.3,106,208.8,130.6,0.8,14.8,8.08,76,71.6,78.3
539
+ "538","0s=suspect Blood Donor",71,"m",14.9,69.8,19.7,95.2,9.8,13.3,2.61,9,7.6,47
540
+ "539","0s=suspect Blood Donor",74,"m",20.3,84,22.8,43,5.7,4.91,3.19,52,218.3,47.8
541
+ "540","0s=suspect Blood Donor",59,"f",19.3,208.2,325.3,146.6,6.9,5.33,4.72,32,295.6,53.1
542
+ "541","1=Hepatitis",38,"m",45,56.3,NA,33.1,7,9.58,6,77.9,18.9,63
543
+ "542","1=Hepatitis",19,"m",41,NA,87,67,12,7.55,3.9,62,65,75
544
+ "543","1=Hepatitis",23,"m",47,19.1,38.9,164.2,17,7.09,3.2,79.3,90.4,70.1
545
+ "544","1=Hepatitis",25,"m",42,38.2,63.3,187.7,14,6,4.28,66.9,40.2,70.5
546
+ "545","1=Hepatitis",27,"m",45,27.5,10.5,37.8,10,8.77,3.2,55.2,35.9,74.5
547
+ "546","1=Hepatitis",29,"m",49,NA,53,39,15,8.79,3.6,79,37,90
548
+ "547","1=Hepatitis",30,"m",45,NA,66,45,14,12.16,6.1,86,43,77
549
+ "548","1=Hepatitis",32,"m",45,34.6,44.3,96.2,16,10.11,6.28,81.2,48.1,82.1
550
+ "549","1=Hepatitis",32,"m",41,34.4,12.1,60.9,6,13.8,5.48,45.4,33.1,71.1
551
+ "550","1=Hepatitis",34,"m",46,36.7,7.4,31.6,9,9.71,5.37,82.3,34.4,71.6
552
+ "551","1=Hepatitis",35,"m",47,37.9,13.3,48.4,8,10.3,4.14,69.2,68.2,76
553
+ "552","1=Hepatitis",36,"m",44,32.9,9.4,32,14,11.42,5.73,68.6,40.6,70.9
554
+ "553","1=Hepatitis",38,"m",41,20.6,15.2,53.5,24,10.23,4.89,81.8,57.9,71.1
555
+ "554","1=Hepatitis",41,"m",42,39.6,26.5,77.6,42,9.67,9.67,57.7,143.4,75.8
556
+ "555","1=Hepatitis",44,"m",49,27.3,40.2,31.1,13,8.91,4.07,81.5,27.6,72.8
557
+ "556","1=Hepatitis",46,"m",48,59.5,11.6,39,7,16.41,4.65,66.4,158.2,72.7
558
+ "557","1=Hepatitis",50,"m",42,41.6,10.2,38.1,17,9.54,7.04,75.3,92.1,72.3
559
+ "558","1=Hepatitis",51,"m",43,37.2,21.4,132.8,5,10.12,5.23,76.2,76.4,76.7
560
+ "559","1=Hepatitis",56,"m",37,114,27.8,324,67,5.75,3.09,97.7,392.2,77.3
561
+ "560","1=Hepatitis",58,"m",43,99.1,12.2,63.2,13,5.95,6.15,147.3,491,65.6
562
+ "561","1=Hepatitis",33,"f",43,29.6,3.8,16.7,6,6.88,5.72,58.8,11.5,78.2
563
+ "562","1=Hepatitis",41,"f",37,31.2,8.2,38.3,7,7.08,5.3,60.8,24.7,82.4
564
+ "563","1=Hepatitis",50,"f",40,32.7,9,46,10,7.51,4.67,56.6,22.3,70.1
565
+ "564","1=Hepatitis",61,"f",50,34.4,27.4,114.4,22,9.48,4.62,61.9,169.8,86
566
+ "565","2=Fibrosis",29,"m",41,43.1,2.4,83.5,6,11.49,5.42,55.2,130,66.5
567
+ "566","2=Fibrosis",40,"m",39,43.1,23.8,114.7,11,9.64,4.2,70.9,127.3,81.3
568
+ "567","2=Fibrosis",46,"m",45,26.9,23.1,125,17,6.97,4.01,60.5,72.2,73
569
+ "568","2=Fibrosis",48,"m",49,45.2,19.3,69.1,30,7.76,4.22,76.7,28.4,72.3
570
+ "569","2=Fibrosis",49,"m",39,NA,118,62,10,7.28,3.5,72,74,81
571
+ "570","2=Fibrosis",49,"m",46,NA,114,75,16,10.43,5.2,72,59,82
572
+ "571","2=Fibrosis",50,"m",42,NA,258,106,15,8.74,4.7,77,80,84
573
+ "572","2=Fibrosis",53,"m",46,NA,34,43,14,8.77,4,112,203,76
574
+ "573","2=Fibrosis",54,"m",41,41.8,41.5,187.9,21,8.59,5.85,91,104.9,79.1
575
+ "574","2=Fibrosis",57,"m",47,29.7,10.2,55.9,12,6.6,4.64,70.9,69.6,80.9
576
+ "575","2=Fibrosis",59,"m",44,34.5,8.9,74.5,6,9.45,4.45,65,95.3,69.7
577
+ "576","2=Fibrosis",64,"m",38,35.7,7.1,41.3,13,7.1,4.52,70,53,66.8
578
+ "577","2=Fibrosis",71,"m",37,NA,130,90,15,9.92,4.7,79,77,76
579
+ "578","2=Fibrosis",36,"f",46,39.3,67.1,161.9,13,9.24,4.81,65.3,60,73.9
580
+ "579","2=Fibrosis",38,"f",40,39.8,14.9,68.9,11,8.55,4.31,60.5,40.1,76.5
581
+ "580","2=Fibrosis",57,"f",43,52.1,8.3,35.8,18,8.61,6.19,71.4,27.9,82
582
+ "581","2=Fibrosis",68,"f",43,22.9,5,42.1,12,7.29,4.89,80.9,11.9,76.1
583
+ "582","2=Fibrosis",49,"f",39,NA,46,39,9,10.21,3.1,89,53,79
584
+ "583","2=Fibrosis",51,"f",37,NA,164,70,9,3.99,4.2,67,43,72
585
+ "584","2=Fibrosis",56,"f",39,NA,42,34,10,7.75,5,80,84,78
586
+ "585","2=Fibrosis",75,"f",36,NA,114,125,14,6.65,NA,57,177,72
587
+ "586","3=Cirrhosis",38,"m",44,NA,94,60,12,4.37,3.2,61,99,77
588
+ "587","3=Cirrhosis",39,"m",34,137.8,4.8,35.6,9,3.65,4.82,519,133.4,57.5
589
+ "588","3=Cirrhosis",41,"m",31,85.3,4.8,60.2,200,1.8,5.34,106.4,151,71.8
590
+ "589","3=Cirrhosis",42,"m",36,69.6,14.9,263.1,40,3.61,3.93,49.6,61,68.6
591
+ "590","3=Cirrhosis",45,"m",29,11.3,7.1,101.9,31,1.73,3.71,76.7,65.6,70
592
+ "591","3=Cirrhosis",46,"m",20,NA,62,113,254,1.48,NA,114,138,NA
593
+ "592","3=Cirrhosis",46,"m",35,109.6,2.3,19.2,11,7.1,4.1,1079.1,105.6,69.1
594
+ "593","3=Cirrhosis",47,"m",42,NA,159,102,11,6.29,5.5,58,201,79
595
+ "594","3=Cirrhosis",51,"m",39,66,29.6,185,19,2,3.6,58.3,399.5,79.4
596
+ "595","3=Cirrhosis",51,"m",33,29.6,4.5,66.6,91,4.02,4.08,75.9,28.5,62.3
597
+ "596","3=Cirrhosis",56,"m",27,81.1,17,319.8,37,1.42,3.54,66.9,93.7,65.3
598
+ "597","3=Cirrhosis",56,"m",23,105.6,5.1,123,43,1.8,2.4,62.7,35.9,62.8
599
+ "598","3=Cirrhosis",56,"m",30,40.4,0.9,80.3,119,1.88,1.43,79.3,17.6,54.2
600
+ "599","3=Cirrhosis",58,"m",31,143.1,7,181.8,58,3.29,3.92,66.4,273.7,78.1
601
+ "600","3=Cirrhosis",59,"m",36,49.7,5.2,110.1,37,2.29,3.68,118.2,56.9,74.8
602
+ "601","3=Cirrhosis",59,"m",27,73.8,4,65.2,209,2.47,3.61,71.7,28.5,60.6
603
+ "602","3=Cirrhosis",59,"m",31,86.3,5.4,95.4,117,1.57,3.51,60.5,53.6,68.5
604
+ "603","3=Cirrhosis",61,"m",39,102.9,27.3,143.2,15,5.38,4.88,72.3,400.3,73.4
605
+ "604","3=Cirrhosis",65,"m",NA,NA,40,54,13,7.5,NA,70,107,79
606
+ "605","3=Cirrhosis",74,"m",23,34.1,2.1,90.4,22,2.5,3.29,51,46.8,57.1
607
+ "606","3=Cirrhosis",42,"f",33,79,3.7,55.7,200,1.72,5.16,89.1,146.3,69.9
608
+ "607","3=Cirrhosis",49,"f",33,190.7,1.2,36.3,7,6.92,3.82,485.9,112,58.5
609
+ "608","3=Cirrhosis",52,"f",39,37,1.3,30.4,21,6.33,3.78,158.2,142.5,82.7
610
+ "609","3=Cirrhosis",58,"f",34,46.4,15,150,8,6.26,3.98,56,49.7,80.6
611
+ "610","3=Cirrhosis",59,"f",39,51.3,19.6,285.8,40,5.77,4.51,136.1,101.1,70.5
612
+ "611","3=Cirrhosis",62,"f",32,416.6,5.9,110.3,50,5.57,6.3,55.7,650.9,68.5
613
+ "612","3=Cirrhosis",64,"f",24,102.8,2.9,44.4,20,1.54,3.02,63,35.9,71.3
614
+ "613","3=Cirrhosis",64,"f",29,87.3,3.5,99,48,1.66,3.63,66.7,64.2,82
615
+ "614","3=Cirrhosis",46,"f",33,NA,39,62,20,3.56,4.2,52,50,71
616
+ "615","3=Cirrhosis",59,"f",36,NA,100,80,12,9.07,5.3,67,34,68
app.py ADDED
@@ -0,0 +1,10 @@
 
 
 
 
 
 
 
 
 
 
 
1
+ import streamlit as st
2
+ import eda
3
+ import prediction
4
+
5
+ navigation = st.sidebar.selectbox('Choose Page : ', ('EDA', 'Patients Diagnose'))
6
+
7
+ if navigation == 'EDA':
8
+ eda.run()
9
+ else:
10
+ prediction.run()
cat_columns.txt ADDED
@@ -0,0 +1 @@
 
 
1
+ ["Sex"]
eda.py ADDED
@@ -0,0 +1,88 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ import streamlit as st
2
+ import pandas as pd
3
+ import seaborn as sns
4
+ import matplotlib.pyplot as plt
5
+ import plotly.express as px
6
+ from PIL import Image
7
+
8
+ # Melebarkan visualisasi untuk memaksimalkan browser
9
+ st.set_page_config(
10
+ page_title='Hepatitis C',
11
+ layout='wide',
12
+ initial_sidebar_state='expanded'
13
+ )
14
+
15
+ def run():
16
+ # Membuat title
17
+ st.title('Patients with Hepatitis C Diagnose')
18
+
19
+ # Menambahkan Gambar
20
+ image = Image.open('hepatitis.jpg')
21
+ st.image(image, caption='Stages of Hepatitis C Disease')
22
+
23
+ # Menambahkan Deskripsi
24
+ st.write('## Background')
25
+ '''
26
+ Hepatitis C is a very dangerous and contagious disease, which is caused by the Hepatitis C virus (HCV).
27
+ Transmission of Hepatitis C can be through body fluids, blood, or when having sex with sufferers.
28
+ Usually, the symptoms of Hepatitis C are not visible, so suddenly the sufferer or patient has experienced the chronic stage of hepatitis.
29
+ Hepatitis C can also trigger the onset of fibrosis which causes cirrhosis and liver cancer.
30
+
31
+ Therefore, the Liver Hospital must have adequate facilities and treatment to treat patients with indications and symptoms of Hepatitis C.
32
+ The purpose of this data classification analysis and modeling is to find out what the diagnoses are in patients so that they receive further treatment, can be cured, and minimize death.
33
+ This modeling uses 4 Machine Learning (Supervised) algorithms, namely Logistic Regression, Support Vector Machine, Random Forest, and Gradient Boosting to get the best predictions with hyperparameter tuning.
34
+ '''
35
+
36
+ st.write('## Dataset')
37
+ '''
38
+ The dataset is from Kaggle, that have 615 rows and 14 columns.
39
+ This column contains information that have a correlation with Hepatitis C disease, such as:
40
+ 1. Bilirubin
41
+ 2. Protein and Albumin
42
+ 3. Creatinine. Elevation in serum creatinine is a common laboratory finding for patients with cirrhosis and can indicate the presence of either an acute kidney injury (AKI) or chronic kidney disease (CKD).
43
+ 4. Alanine transaminase (ALT): ALT is an enzyme found in the liver that helps convert proteins into energy for the liver cells. When the liver is damaged, ALT is released into the bloodstream and levels increase.
44
+ 5. Aspartate transaminase(AST): AST is an enzyme that helps metabolize amino acids. Like ALT, AST is normally present in blood at low levels. An increase in AST levels may indicate liver damage, disease or muscle damage.
45
+ 6. Alkaline phosphatase (ALP): ALP is an enzyme found in the liver and bone and is important for breaking down proteins. Higher-than-normal levels of ALP may indicate liver damage or disease, such as a blocked bile duct, or certain bone diseases.
46
+ 7. Albumin and total protein:: Albumin is one of several proteins made in the liver. Your body needs these proteins to fight infections and to perform other functions. Lower-than-normal levels of albumin and total protein may indicate liver damage or disease.
47
+ 8. Gamma-glutamyltransferase (GGT): GGT is an enzyme in the blood. Higher-than-normal levels may indicate liver or bile duct damage.
48
+ '''
49
+
50
+ # Membuat Garis Lurus
51
+ st.markdown('---')
52
+
53
+ # Membuat Sub Headrer
54
+ st.subheader('EDA for Analyze Patients')
55
+
56
+ # Magic Syntax
57
+ '''
58
+ On this page, the author will do a simple exploration.
59
+ The dataset used is the Hepatitis C dataset.
60
+ This dataset comes from Kaggle.
61
+ '''
62
+
63
+ # Show DataFrame
64
+ df1 = pd.read_csv('HepatitisCdata.csv')
65
+ st.dataframe(df1)
66
+
67
+ # Membuat Barplot
68
+ st.write('#### Diagnose Plot')
69
+ fig = plt.figure(figsize=(10,7))
70
+ sns.countplot(x='Category', data=df1, palette="PuRd")
71
+ st.pyplot(fig)
72
+
73
+ # Membuat heatmap correlation
74
+ st.write('#### Heatmap Correlation')
75
+ fig = plt.figure(figsize = (15,8))
76
+ sns.heatmap(df1.corr(), annot = True, square = True)
77
+ st.pyplot(fig)
78
+
79
+ # Membuat Histogram Berdasarkan Input User
80
+ st.write('#### Histogram Based On User Input')
81
+ pilihan = st.selectbox('Choose Column : ', ('Age', 'Sex', 'AST',
82
+ 'CHE', 'BIL', 'GGT'))
83
+ fig = plt.figure(figsize=(15,5))
84
+ sns.histplot(df1[pilihan], bins=30, kde=True)
85
+ st.pyplot(fig)
86
+
87
+ if __name__ == '__main__':
88
+ run()
enc_columns.txt ADDED
@@ -0,0 +1 @@
 
 
1
+ ["f", "m"]
hepatitis.jpg ADDED
main.py ADDED
@@ -0,0 +1,10 @@
 
 
 
 
 
 
 
 
 
 
 
1
+ import streamlit as st
2
+ import eda
3
+ import prediction
4
+
5
+ navigation = st.sidebar.selectbox('Choose Page : ', ('EDA', 'Patients Diagnose'))
6
+
7
+ if navigation == 'EDA':
8
+ eda.run()
9
+ else:
10
+ prediction.run()
norm_columns.txt ADDED
@@ -0,0 +1 @@
 
 
1
+ ["Age", "CHOL"]
num_columns.txt ADDED
@@ -0,0 +1 @@
 
 
1
+ ["Age", "ALB", "ALP", "ALT", "AST", "BIL", "CHE", "CHOL", "CREA", "GGT", "PROT"]
pipelines.pkl ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:b31e96097da3fe87b57dac33a4415a2e584f7c75e5fdd3628e6fab3c10c85558
3
+ size 3734
prediction.py ADDED
@@ -0,0 +1,76 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ import streamlit as st
2
+ import pandas as pd
3
+ import numpy as np
4
+ import pickle
5
+ import json
6
+
7
+ # Load All Files
8
+
9
+ with open('pipelines.pkl', 'rb') as file_1:
10
+ pipelines = pickle.load(file_1)
11
+
12
+ with open('num_columns.txt', 'r') as file_2:
13
+ num_columns = json.load(file_2)
14
+
15
+ with open('cat_columns.txt', 'r') as file_3:
16
+ cat_columns = json.load(file_3)
17
+
18
+ with open('norm_columns.txt', 'r') as file_4:
19
+ norm_columns = json.load(file_4)
20
+
21
+ with open('skew_columns.txt', 'r') as file_5:
22
+ skew_columns = json.load(file_5)
23
+
24
+ with open('enc_columns.txt', 'r') as file_6:
25
+ enc_columns = json.load(file_6)
26
+
27
+ def run():
28
+ with st.form(key='Hepatitis_C_Prediction'):
29
+ Category = st.selectbox('Category', (0, 1, 2, 3, 4), index=1)
30
+ Age = st.number_input('Age', min_value=23, max_value=80, value=23)
31
+ Sex = st.selectbox('Sex', ('m', 'f'), index=1)
32
+ st.markdown('---')
33
+
34
+ ALB = st.number_input('ALB', min_value=0, max_value=500, value=0)
35
+ ALP = st.number_input('ALP', min_value=0, max_value=500, value=0)
36
+ ALT = st.number_input('ALT', min_value=0, max_value=500, value=0)
37
+ AST = st.number_input('AST', min_value=0, max_value=500, value=0)
38
+ BIL = st.number_input('BIL', min_value=0, max_value=500, value=0)
39
+
40
+ st.markdown('---')
41
+
42
+ CHE = st.number_input('CHE', min_value=0, max_value=100, value=0)
43
+ CHOL = st.number_input('CHOL', min_value=0, max_value=100, value=0)
44
+ CREA = st.number_input('CREA', min_value=0, max_value=1000, value=0)
45
+ GGT = st.number_input('GGT', min_value=0, max_value=1000, value=0)
46
+ PROT = st.number_input('PROT', min_value=0, max_value=1000, value=0)
47
+
48
+ submitted = st.form_submit_button('Predict')
49
+
50
+ data_inf = {
51
+ 'Category': Category,
52
+ 'Age': Age,
53
+ 'Sex': Sex,
54
+ 'ALB': ALB,
55
+ 'ALP': ALP,
56
+ 'ALT': ALT,
57
+ 'AST': AST,
58
+ 'BIL': BIL,
59
+ 'CHE': CHE,
60
+ 'CHOL': CHOL,
61
+ 'CREA': CREA,
62
+ 'GGT': GGT,
63
+ 'PROT': PROT
64
+ }
65
+
66
+ data_inf = pd.DataFrame([data_inf])
67
+ st.dataframe(data_inf)
68
+
69
+ if submitted:
70
+
71
+ # Predict using Linear Regression
72
+ y_pred_inf = pipelines.predict(data_inf)
73
+ st.write('# Diagnose : ', str(int(y_pred_inf)))
74
+
75
+ if __name__ == '__main__':
76
+ run()
requirements.txt ADDED
@@ -0,0 +1,9 @@
 
 
 
 
 
 
 
 
 
 
1
+ # Berisi daftar library yang kita butuhkan
2
+
3
+ streamlit
4
+ pandas
5
+ seaborn
6
+ matplotlib
7
+ numpy
8
+ scikit-learn==1.2.1
9
+ plotly
skew_columns.txt ADDED
@@ -0,0 +1 @@
 
 
1
+ ["ALB", "ALP", "ALT", "AST", "BIL", "CHE", "CREA", "GGT", "PROT"]